Solent University
SCHOOL OF MEDIA, ARTS AND TECHNOLOGIE
BSc (Hons) Popular Music Production
2023 / 2024
Sergio Quiroga Ortego
“Delivering High Fidelity Audio for Streaming
Platforms”
Supervisor
:
Neil Kennedy
Date of submission
:
May 2024
1
Acknowledgements
First of all, I would like to extend my deepest gratitude to my family, who
have consistently supported me throughout my academic journey. Their
unwavering encouragement has been a cornerstone of my progress. I am also
profoundly thankful to my initial mentors in the field of music production. These
individuals were not merely educators but also guides who shepherded me
through both the technical intricacies and the ethical dimensions of the
profession. Their invaluable advice and relentless support have been
instrumental in my continuous personal and professional development. Lastly, but
certainly not least, I owe a significant debt of gratitude to the wider community of
audio engineers, producers, and related professionals. This vibrant collective has
always been a rich source of education, fostering an environment ripe with debate
and collaboration. Their collective wisdom and camaraderie have greatly
enriched my educational experience.
2
Contents
Acknowledgements .................................................................................................... 2
Contents ...................................................................................................................... 3
List of Tables and Figures .......................................................................................... 5
Literature Review ........................................................................................................ 6
1.
Introduction.......................................................................................................... 8
1.1 Streaming Growth in Music Distribution ......................................................... 8
1.2 Audio quality and listener experience ........................................................... 10
1.3 Goals ................................................................................................................ 12
2.
Streaming Platforms and Loudness ................................................................. 14
2.1 Loudness Standards ....................................................................................... 14
2.2 The Loudness Normalization Process ........................................................... 15
2.3 Album and Track Normalization of Music for Distribution............................ 16
2.4 Normalization Workflow .................................................................................. 18
2.5 Formats and Codecs ....................................................................................... 19
3.
Human Hearing .................................................................................................. 22
3.1 Our Ears ........................................................................................................... 22
3.2 Audible Spectrum ............................................................................................ 24
3.3 Dynamic Range ............................................................................................... 27
4.
Pursuing Optimal Audio Quality for Playback ................................................. 29
4.1 Sampling Theorem .......................................................................................... 29
4.2 Aliasing ............................................................................................................ 32
4.3 High-Resolution Audio or Higher Sample Rates for Playback ..................... 34
4.4 Higher Sample Rates Being Harmful for Playback........................................ 36
4.5 Oversampling .................................................................................................. 39
4.6 Bit Depth .......................................................................................................... 40
4.7 How to Actually Improve Audio Quality for the End User ............................. 43
5.
Modern Production Workflows ......................................................................... 44
5.1 Production Frameworks ................................................................................. 44
5.1.1 Recording Sample Rates and Bit Depths ................................................ 45
5.1.2 Production Sample Rates and Bit Depths ............................................... 46
5.2 Loudness ......................................................................................................... 49
5.3 Good Practices ................................................................................................ 53
3
6. Conclusion ............................................................................................................ 56
7. Bibliography .......................................................................................................... 60
8. Appendices ............................................................................................................. A
8.1 Appendix A: Lossless Distribution .................................................................. A
8.2 Appendix B: High-Frequency Auditory Perception and its Impact on HighEnd Audio Content Sampling ................................................................................. B
8.3 Appendix C: Considerations for Antialiasing Linear Phase Filters............... C
8.4 Appendix D: Higher Sample Rates association with Professional Audio..... D
8.5 Appendix E: Nuances of Loudness Perception............................................... E
4
List of Tables and Figures
Figure 1: Stacked Bar Graph of Global Recorded Music Industry Revenues
1999-2023 (US$ Billions). .................................................................................. 9
Figure 2: Pie Chart of Global Recorded Music Revenues by Segment 2023. . 10
Figure 3: Multi-Line Graph of Listener’s Behavior After a Change in Loudness
Occurred IF the Change Was Frequent.............................................................11
Figure 4: Table of Max Integrated Loudness and TP Requirements of
Streaming Platforms. ........................................................................................ 15
Figure 5: Illustration of the Normalization Process. ......................................... 16
Figure 6: Illustration of the Album and Track Normalization Techniques.......... 18
Figure 7: Flow Chart of Content Delivery Workflows. ...................................... 18
Figure 8: Schematic illustration of the auditory anatomy. ................................ 23
Figure 9: Multi-Line Graph of Fletcher-Munson Loudness level Contours. ...... 25
Figure 10: Multi-Line Graph of Equal Loudness contours (2003 revision). ...... 26
Figure 11: Graphically representation as a rough stairstep pattern (depicted in
red) of a sampled signal. .................................................................................. 30
Figure 12: Graphically representation as a rough stairstep pattern (depicted in
red) of high frequencies sampled signal. .......................................................... 30
Figure 13: Illustration of distortion products resulting from intermodulation of a
30kHz and a 33kHz tone. ................................................................................. 36
Figure 14: Diagram illustrating the transition band width available for a 48kHz
ADC/DAC (left) and a 96kHz ADC/DAC (right). ................................................ 40
Figure 15: Illustration of How Inter-Sample Peaks Occurred in the Analog
Domain. ............................................................................................................ 53
5
Literature Review
•
Aliasing distortion: Audio distortion that occurs when a signal containing
frequencies above the Nyquist frequency is improperly sampled or
represented in digital form. It results in the appearance of spurious or false
frequencies in the digital signal, which are not present in the original
analog signal.
•
Audio quality: perceived fidelity, clarity, and overall fidelity of sound
reproduction in an audio recording or playback.
•
A-weighted decibels (dBA): Unit of measurement used to quantify sound
levels, taking into account the sensitivity of the human ear to different
frequencies.
•
Bit Depth: Number of bits used to represent the amplitude of each sample
in a digital audio signal.
•
Clipping: Distortion that occurs when an audio signal exceeds the
maximum amplitude that can be represented without distortion, typically in
digital audio systems.
•
Decibels Sound Pressure Level (dBS SPL): Unit of measurement used
to quantify the intensity or loudness of a sound relative to the threshold of
human hearing.
•
Formats: Specific encoding methods used to represent and store audio
data digitally.
6
•
Intermodulation distortion: Type of nonlinear distortion that occurs in
audio systems when two or more different frequencies interact within the
system and produce additional frequencies that were not present in the
original input signals.
•
Loudness Target: Predetermined level or range of perceived loudness
that audio content is intended to achieve during recording, mixing,
mastering, or playback. It is a reference point used in audio production to
ensure consistency and uniformity in the perceived loudness of audio
material across different tracks, albums, or media formats.
•
Loudness: Subjective perception of the intensity or volume of sound.
•
Music streaming services: Digital platforms that enable users to access
and listen to music over the internet without the need for downloading.
•
Normalization: Process of adjusting the amplitude of an audio signal to
bring its peak or average level to a predetermined target value.
•
Nyquist frequency: The Nyquist frequency is defined as half of the
sampling rate of a digital system. In other words, it is the highest frequency
that can be accurately represented in a digital signal sampled at a given
rate.
•
Peak (levels): Maximum instantaneous amplitude of an audio signal over
a specified time period, typically measured in decibels (dB) relative to a
reference level.
•
Sample Rate: Number of samples of audio taken per second, typically
measured in Hertz (Hz).
7
•
True Peak: Maximum instantaneous amplitude of an audio signal when
considering inter-sample peaks, which are peaks that occur between the
discrete samples of a digital audio signal.
1. Introduction
1.1 Streaming Growth in Music Distribution
It is well known that music streaming services have experienced significant
growth in recent years as a means of music consumption. This trend has been
extensively discussed by the International Federation of the Phonographic
Industry (IFPI) (2024).
In 2024, as part of its annual report on the phonographic industry, the IFPI
released the Global Music Report of 2023. This report sheds light on various
observations regarding consumption formats and trends, as well as the markets
in which they are prevalent.
In 2023, the global recorded music market demonstrated robust growth,
increasing by 10.2%, marking the second-highest growth rate ever recorded.
With a total value of US$28.6 billion, this growth marked the ninth consecutive
year of expansion. Streaming formats, particularly subscription streaming,
emerged as the primary drivers of revenue growth, constituting the majority of
both revenue growth and market share. Streaming revenues globally increased
by 10.4% to reach US$19.3 billion, serving as the primary driver of overall global
8
growth. Streaming accounted for over two-thirds (67.3%) of the total global
market, underscoring streaming platforms as the dominant method of music
consumption.
Figure 1: Stacked Bar Graph of Global Recorded Music Industry Revenues 1999-2023 (US$
Billions).
9
Figure 2: Pie Chart of Global Recorded Music Revenues by Segment 2023.
1.2 Audio quality and listener experience
The emergence of streaming platforms as primary conduits for music
dissemination has undeniably revolutionized the landscape of audio recording,
mixing, post-production, and delivery workflows. However, this paradigm shift has
brought to the forefront a myriad of challenges concerning audio quality and user
experience.
Intrinsic to the realm of sound are variations in loudness, a phenomenon that has
been subject to extensive perceptual studies. These studies have elucidated that
listeners generally exhibit a degree of tolerance towards moderate fluctuations in
audio content. Nevertheless, there exists a discernible aversion towards
10
pronounced shifts in loudness, particularly when transitioning between unrelated
audio segments, such as the interruption of a television or radio program by
commercial announcements or the switch between channels or audio sources.
The concept of maintaining a consistent loudness level, often termed the "comfort
zone," emerges as paramount in mitigating listener discomfort stemming from
abrupt changes in audio dynamics. It is imperative to recognize that the excessive
employment of loud masters can precipitate audio distortion during transmission
in lossy formats, thereby compromising overall audio fidelity.
Figure 3: Multi-Line Graph of Listener’s Behavior After a Change in Loudness Occurred IF the
Change Was Frequent.
Thus, the imperative arises for the development and implementation of a
loudness-based leveling solution within streaming platforms, complemented by
the establishment of an appropriate loudness target. This strategic approach not
only serves to preserve optimal audio quality but also engenders heightened user
satisfaction by fostering a seamless and immersive listening experience across
diverse audio contexts. Consequently, the integration of such measures
11
represents a pivotal step towards harmonizing audio quality and listener
experience within the dynamic landscape of streaming media.
1.3 Goals
In response to the aforementioned challenges, the primary objective of this
document is to furnish the requisite knowledge essential for comprehending the
nuances
therein,
thereby
guiding
stakeholders
through
a
series
of
recommendations and guidelines. These directives are meticulously crafted with
the aim of delineating and implementing an optimal Distribution Loudness
framework tailored specifically for streaming and on-demand audio file playback.
By adhering to these recommendations, stakeholders can elevate the overall
listening experience, fostering consistency and fidelity across a spectrum of audio
platforms and content categories.
The overarching goals of this endeavour are delineated as follows:
•
Comprehensive Understanding of Streaming Platforms: Delve into the
intricacies of streaming platforms to elucidate their operational
mechanisms and underlying principles, thereby facilitating informed
decision-making and strategic planning.
•
Profound
Insight
understanding
of
into
Digital
Audio:
the
fundamental
Acquire
a
comprehensive
concepts
and
technologies
underpinning digital audio.
•
Establishment of Optimal Audio Formats: Identify and define the optimal
format for high-fidelity audio playback.
12
•
Enhancement of Listener Quality and Experience: Prioritize the
enhancement of listener quality and experience by implementing
measures aimed at mitigating audio artifacts, optimizing dynamic range,
and ensuring consistency in loudness levels across diverse audio
contexts.
•
Appreciation of Sample Rate and Bit Depth Dynamics: Recognize the
pivotal role played by sample rate and bit depth in shaping modern
production workflows, thereby enabling stakeholders to make informed
decisions regarding audio quality and fidelity.
•
Provision
of
Recommendations
and
Best
Practices:
Offer
a
comprehensive set of recommendations and best practices aimed at
empowering stakeholders to achieve exemplary standards of audio quality
in their productions.
13
2. Streaming Platforms and Loudness
2.1 Loudness Standards
In contemporary audio mastering, strict adherence to loudness standards
is imperative. However, navigating these standards can be intricate due to the
nuanced differences among various streaming platforms. Therefore, one of the
primary objectives of this study is to provide a comprehensive overview of the
requirements outlined by leading streaming services, elucidating their
methodologies concerning format compatibility and loudness management.
At the core of this investigation lies the technical document AESTD1008.1.21-9,
which supersedes AESTD1004 and serves as the foundation for crafting
recommendations for loudness of internet audio streaming and on-demand
distribution. Through meticulous examination, the aim of this research is to
elucidate the intricacies of loudness normalization processes and the
methodologies embraced by platforms such as Spotify and Apple Music.
To commence, it is imperative to grasp the requirements set by streaming
platforms regarding the loudness and peak levels of the audio content intended
for submission. To facilitate this understanding, a table containing the relevant
values pertinent to this research has been compiled.
14
Figure 4: Table of Max Integrated Loudness and TP Requirements of Streaming Platforms.
2.2 The Loudness Normalization Process
In the implementation of normalization, streaming services conduct an
analysis of the original audio's loudness to ascertain whether it surpasses or falls
short of the desired distribution loudness. Should the original audio exceed this
specified threshold, normalization is employed to attenuate its level, ensuring
adherence to the desired standard, a process commonly referred to as downward
normalization. Conversely, if the loudness falls below the desired level,
normalization introduces positive gain, termed upward normalization, which may
necessitate the utilization of peak limiting techniques to prevent clipping.1
In situations where upward normalization poses a risk of clipping, the
incorporation of peak limiting becomes essential to maintain the integrity of the
1 While many platforms provide the option to toggle normalization on or off, not all platforms have
this feature enabled by default.
15
audio signal, albeit potentially altering its sonic characteristics. Distributors may
opt for partial normalization to preserve more of the audio's dynamics, as
illustrated in the accompanying figure labeled "Partially Normalized".
Figure 5: Illustration of the Normalization Process.
2.3 Album and Track Normalization of Music for Distribution
In the pursuit of normalization for individual tracks or entire albums,
streaming platforms employ various Music Normalization Techniques. One such
technique, album normalization, assumes a pivotal role in preserving the original
loudness intent of a mastered album and, consequently, its artistic balance or
intent. Through album normalization, streaming services adjust the loudness of
all tracks within an album by applying the same gain value, thereby maintaining
the relative "album loudness" of the tracks.
As per the recommendations delineated in the TD1008 report by the AES, it is
advisable that the album loudness value be derived from the loudest track present
on the album, rather than from a comprehensive loudness measurement of the
16
entire album. This approach ensures consistency in loudness across all tracks
within the album, thereby aligning with the intended sonic experience envisioned
by the artist or mastering engineer.
The recommendations outlined in the AES 1008 report emphasize the significant
role of album normalization as a standard practice, even in situations where
tracks are played outside the album context, such as in shuffle play mode. This
practice stems from the deliberate mastering of quieter tracks within albums to
maintain a correct relative balance with louder tracks. Consequently, the
loudness of quieter tracks is intentionally attenuated. Research indicates that
listeners exhibit a preference for experiencing tracks at their intended relative
levels, even when encountered outside the album sequence.
It is advisable for distributors to normalize the loudest track of an album to -14
LUFS, typically resulting in an integrated album loudness of -16 LUFS,
considering the tendency for the loudest tracks to be approximately 2 LU louder
than the album's average loudness. Adhering to this procedure ensures that the
majority of quieter tracks maintain a loudness level above -20 LUFS. While Album
Normalization serves as the preferred default approach, in cases where
implementation proves impractical, it is recommended to normalize all tracks to 16 LUFS to maintain consistency across the album's playback experience.
17
Figure 6: Illustration of the Album and Track Normalization Techniques.
Figure 6: Illustration of the Album and Track Normalization Techniques.
2.4 Normalization Workflow
In the domain of distributing file-based audio to consumers, loudness
normalization is often automated. Within this framework, content is categorized
into two main groups: music-only or assorted, which encompasses a mixture of
music, speech, interstitials, and similar elements. It is noteworthy that music-only
content undergoes a distinct normalization process.
Figure 7: Flow Chart of Content Delivery Workflows.
18
2.5 Formats and Codecs
Presently, the predominant method employed by streaming platforms
involves distributing their catalogues in lossy formats such as MP3, AAC, or
OGG. These formats are favoured for their compact file size and efficient
streaming capabilities. Lossy formats compress audio data, thereby reducing file
size, facilitating storage and seamless music streaming, ultimately contributing to
a smoother listening experience. Additionally, they boast extensive compatibility
with a wide array of devices and platforms, presenting a cost-effective solution
for production and distribution compared to lossless formats.
However, it is noteworthy that music streaming platforms have expanded
their offerings to include a variety of formats to cater to diverse user preferences
and device compatibility.
In the realm of digital data, compression entails the process of reducing
file size by eliminating redundant or unnecessary information. Lossy and lossless
audio compression represent distinct techniques for reducing the size of audio
files. The primary differentiation lies in their approach to compressing audio data.
Lossy Compression:
Lossy compression entails the discarding of certain audio data to reduce
the file size. This approach is adopted when a significant reduction in file size is
required, and a certain degree of quality loss is acceptable. Algorithms utilized
for lossy compression analyse the audio signal and eliminate components
considered less perceptible to the human ear. By removing specific audio
19
information and simplifying the data, lossy compressed audio formats achieve
further reduction in file size, albeit at the expense of audio quality.
However, various technologies are employed to selectively remove
portions of the sound that have minimal impact on perceived quality. These
techniques also strive to minimize the introduction of audible noise during the
compression process.
Lossy Formats:
•
MP3: A lossy compression format, widely used for music streaming, with
a bit rate of 128 kbps to 320 kbps.
•
AAC (Advanced Audio Coding): A lossy compression format, used by
Apple Music, with a bit rate of 128 kbps to 320 kbps.
•
OGG (Ogg Vorbis): A lossy compression format, with a bit rate of 128 kbps
to 320 kbps.
Lossless Compression:
In contrast to lossy compression, lossless compression reduces file size
by eliminating extraneous metadata while preserving all audio data intact.
Algorithms utilized for lossless compression condense audio data without any
loss of information, resulting in a smaller file size without compromising quality.
Lossless compressed audio formats effectively store data in reduced space while
enabling the recreation of the original and uncompressed data from the
compressed version.
Unlike uncompressed audio formats, which allocate the same number of
bits per unit of time to encode both sound and silence, lossless compressed
20
formats exhibit a notable distinction. Encoding a minute of absolute silence in a
lossless compressed format yields a file occupying minimal space, unlike
uncompressed formats where silence consumes the same space as sound.
Consequently, a lossless audio file is smaller than its uncompressed counterpart.
Therefore, lossless music formats aim to minimize processing time while
maintaining high audio fidelity.
Lossless Formats:
•
FLAC (Free Lossless Audio Codec): A lossless compression format,
supports sample rates up to 192kHz, bit depth up to 24-bit.
•
ALAC (Apple Lossless Audio Codec): A lossless compression format,
used by Apple Music, supports sample rates up to 192kHz, bit depth up to
24-bit.
Uncompressed Formats:
•
WAV (Waveform Audio File Format): An uncompressed format, supports
sample rates up to 192kHz, bit depth up to 32-bit.
•
AIFF (Audio Interchange File Format): An uncompressed format, supports
sample rates up to 192kHz, bit depth up to 32-bit.
It is important to note that lossless formats are not directly supported by
the Bluetooth standard. Bluetooth audio codecs, such as AAC, aptX, and aptX
HD, employ lossy compression techniques to reduce the bitrate of the audio
signal for improved compression and transmission efficiency. Although these
codecs aim to balance quality and compression ratio, they do not support lossless
audio.
21
In summary, lossy compression reduces file size by discarding some audio
data, while lossless compression eliminates unnecessary metadata without
discarding any audio data.
3. Human Hearing
3.1 Our Ears
In order to ascertain the optimal format for audio playback, it is imperative
to delve into the intricacies of our auditory system, which fundamentally dictates
our perception of sound. The journey of a sound wave begins with its reception
by the external ear, acting as the initial gateway to our auditory experience. As
these waves propagate through the ear canal, they encounter the eardrum,
initiating vibrations.
These mechanical vibrations set off a sequence of events within the middle ear,
orchestrated by the Malleus, Incus, and Stapes, tiny yet indispensable bones that
facilitate the transmission of sound energy. They modulate the sound's volume
and pitch characteristics before conveying it to the cochlea, our auditory sensory
organ.
Within the cochlea lies the remarkable basilar membrane, akin to a resilient sheet
housing specialized hair cells. These cells function as miniature sensors, each
attuned to a specific range of sounds, distributed along the basilar membrane in
22
a gradient fashion. This arrangement enables us to perceive a wide spectrum of
sounds, ranging from low-frequency rumbles to high-pitched tones.
Figure 8: Schematic illustration of the auditory anatomy.
The phenomenon of frequency selectivity demonstrated by cochlear hair
cells is of paramount importance in understanding our perception of sound. This
selectivity dictates that certain regions of the basilar membrane respond most
vigorously to particular frequencies, creating distinct peaks of sensitivity within
the auditory spectrum. Moreover, the overlapping nature of these frequency
bands ensures a seamless transition between different auditory stimuli, allowing
for the integration of complex sound scenes with remarkable fidelity.
However, it is imperative to acknowledge the inherent limitations of our auditory
apparatus. Despite its remarkable capabilities, the auditory system is bound by
constraints that impact our perception of sound. One such constraint is the
presence of a critical threshold beyond which sounds become imperceptible to
23
the human ear. This threshold is determined by the absence of hair cells
specialized to detect specific frequencies, rendering certain sounds effectively
inaudible.
3.2 Audible Spectrum
Exploring the range of sounds perceptible to humans involves identifying
both the highest and lowest frequencies within our auditory spectrum. This
determination hinges on understanding the threshold at which sounds transition
from barely audible to uncomfortably loud. In the early 1930s, Harvey Fletcher
and Wilden A. Munson addressed this challenge in their seminal paper titled
"Loudness, its definition, measurement, and calculation," published in 1933. Their
work culminated in the creation of the influential "Fig. 4 Loudness level contours,"
which remain foundational in auditory research.
Drawing upon the Fletcher-Munson curves and contemporary data, the
consensus on human hearing range typically spans from 20 Hz to 20 kHz. This
range, spanning nearly a century of empirical research, comprehensively covers
the audible spectrum. It serves as a fundamental benchmark for audio
reproduction, ensuring fidelity and accuracy in capturing and reproducing sounds
within the human auditory range.
24
Figure 9: Multi-Line Graph of Fletcher-Munson Loudness level Contours.
Recent revisions (ISO, 2003) to auditory standards have brought attention
to our sensitivity to high-frequency sounds, particularly suggesting that our
perception may be less acute in the upper range, specifically around 16 kHz, than
previously assumed.
25
Figure 10: Multi-Line Graph of Equal Loudness contours (2003 revision).
It is crucial to recognize that our perception extends beyond the
conventional auditory range, even when sounds fall outside our hearing
capabilities due to the absence of hair cells sensitive to their frequencies. An
illustrative example of this phenomenon is evident in subsonic frequencies
generated by seismic activity, such as earthquakes. These seismic waves often
possess frequencies below the threshold of human hearing, typically below 20
Hz, rendering them inaudible to our ears. However, despite their subsonic nature,
we can still perceive their effects through other sensory modalities. For instance,
we may feel the vibrations transmitted through the ground, demonstrating the
multifaceted nature of our perception beyond traditional auditory means.
However, for the purpose of this study, our focus will remain exclusively
on the audible spectrum of human hearing.
26
3.3 Dynamic Range
Now, let's explore the range between the quietest and loudest sounds
perceivable to us. One approach to defining this absolute range involves
examining the curves representing the absolute threshold of hearing and the
threshold of pain. The difference in decibel levels between the threshold of pain
and the absolute threshold of hearing is approximately 140 decibels for a young,
healthy listener. However, it is crucial to note that exposure to sound levels
exceeding 130 decibels can lead to permanent hearing damage within seconds
to minutes, emphasizing the critical importance of adhering to safe listening
practices.
Nevertheless, the expansive dynamic range of 140/130 dB is somewhat
idealistic, given the practical limitations encountered in everyday environments.
Such ranges are often observed in studies conducted within anechoic chambers,
where ambient noise is virtually absent, allowing for the detection of even the
faintest sounds, such as one's heartbeat or blood circulation. However, achieving
this level of precision requires specialized equipment, meticulous calibration, and
controlled conditions that diverge significantly from the typical urban environment
characterized by continuous noise pollution.
To provide context, a quiet broadcasting or recording studio, or a sound isolation
room, typically registers a sound pressure level of around 20 dBSPL, which is
approximately 28 dB louder than the quietest audible sound. For example, a study
conducted by the National Institutes of Health (NIH) in 2016 investigated ambient
noise level variations using an A-weighted sound level meter across four zones
27
in Vadodara city. Over a span of three hours, measurements were taken at 15minute intervals for three consecutive days, consistently at the same time. The
findings revealed the highest equivalent noise level of 93.7 dBA in the commercial
zone, followed by 85.5 dBA in the industrial zone, 73.2 dBA in the silence zone,
and 70.2 dBA in the residential zone.2
In summary, the human auditory system perceives sound within the
audible spectrum, ranging from 20 Hz to 20 kHz, with a dynamic range typically
spanning between the threshold of hearing and the threshold of pain, totaling
approximately 130 to 140 dB. This delineation holds paramount significance as it
profoundly influences technical considerations in audio engineering, particularly
regarding digital audio systems. These considerations include determining the
appropriate sample rate and bit depth to accurately capture and reproduce analog
sound information.
Now, let us transition into the exploration of the process of encoding
analog information into the digital domain, commonly referred to as sampling
theory.
2 The World Health Organization (WHO) defines noise above 65 decibels (dBA) as noise
pollution.
28
4. Pursuing Optimal Audio Quality for Playback
4.1 Sampling Theorem
Analog waves, such as sinusoidal, represent continuous functions over
time. Therefore, to sample them, we need to capture a sufficient number of
unique and distinct signal values that a sinusoidal function can produce. This is
achieved by sampling the signal at discrete points in time, converting it into a
discrete signal.
The process of converting a continuous signal into a discrete signal is governed
by the Nyquist-Shannon Sampling Theorem (1928). This theorem states that any
band-limited continuous-time signal can be accurately converted to and from
digital signals when sampled at a rate at least twice as high as the highest
frequency of the signal to be reproduced.
With human auditory perception typically capped at around 20kHz, a sampling
rate exceeding 40kHz becomes imperative for faithfully capturing the audible
frequency spectrum.
In visual representations, sampled signals often exhibit jagged stair-step
patterns,
which,
while
appearing
as
coarse
approximations,
maintain
mathematical precision. Upon conversion back to analog form, these signals
faithfully restore their original smooth waveform.
29
Common understanding holds that sampled signals resemble rough stair-step
approximations of the continuous waveform. It is often assumed that increasing
the sampling rate, along with the number of bits per sample, yields finer
representations and brings digital signals closer to their analog counterparts.
However, it is crucial to clarify that while signals below the Nyquist frequency are
captured comprehensively through sampling alone, an infinite sampling rate is
unnecessary for lossless reconstruction of the analog signal with precise timing
and fidelity.
Figure 11: Graphically representation as a rough stairstep pattern (depicted in red) of a
sampled signal.
Figure 12: Graphically representation as a rough stairstep pattern (depicted in red) of high
frequencies sampled signal.
30
To correct misconceptions about depicting digital waveforms, it is crucial
to recognize that representing a digital waveform as a stair-step is inaccurate. A
stair-step function is continuous in time, jagged, and piecewise, with a defined
value at every point in time. However, a sampled signal operates on a discrete
time scale, possessing a value only at each instantaneous sample point and
remaining undefined elsewhere. Consequently, there is no value assigned to the
signal between sample points. Therefore, a more appropriate visualization of a
discrete time signal is a lollipop graph, which accurately reflects its discrete nature
and emphasizes the discrete sample points rather than attempting to interpolate
values between them.
This misconception may primarily stem from engineers employing zeroorder hold techniques when representing audio signals for the sake of
convenience and practicality. Additionally, digital audio workstations (DAWs)
often connect discrete points of a waveform graph with a straight line between
points for convenience, creating a more user-friendly waveform for interpretation
and manipulation.
All signals with content entirely below the Nyquist frequency (half the
sampling rate) are perfectly and completely captured by sampling, without the
need for an infinite sampling rate. Sampling preserves frequency response and
phase, allowing for lossless reconstruction of the analog signal with precise
timing.
31
Real-world complications arise in audio sampling, with one of the most
significant challenges being the necessity for band-limiting to prevent aliasing
distortion. Signals containing frequencies above the Nyquist frequency must
undergo lowpass filtering before sampling, typically achieved through an analog
anti-aliasing filter.
However, these filters do not immediately cut off all frequencies at the specified
cutoff. They require some space to ramp down and progressively attenuate them.
To accommodate this, a buffer or "guard band" between the highest frequency
content we want to represent, and the Nyquist frequency is necessary. This buffer
is why we need sampling rates higher than twice the Nyquist frequency, as
established by the Nyquist-Shannon Sampling Theorem.
For these reasons and others, sampling rates of 44.1 kHz and 48 kHz are
commonly used as standards.
4.2 Aliasing
Aliasing manifests within discrete signal representations, dictated by
sampling parameters, wherein fewer than two data points encapsulate a single
cycle of the signal. Consequently, the sampled signal forfeits the capability to
faithfully reproduce frequencies beyond the Nyquist limit. Despite the loss of
fidelity, the underlying data persists within the sampled signal, albeit in a distorted
form.
32
Consider, for example, the introduction of distortion, a phenomenon that
engenders harmonics harmonically aligned with the original signal, perceptually
enhancing its timbral richness. However, should these additional harmonics
surpass the Nyquist frequency threshold, they undergo folding back into the
spectrum of the sampled signal, precipitating foldback distortion, commonly
referred to as aliasing distortion.
It is noteworthy that foldback distortion begets extraneous harmonics
devoid of harmonic alignment with the original signal, thereby eliciting auditory
artifacts perceived as deleterious to the overall fidelity and quality of the audio
signal in the majority of instances. This divergence from harmonic coherence
underscores the adverse implications of aliasing distortion, accentuating the
imperative for meticulous management of sampling parameters to mitigate its
deleterious effects on audio fidelity and perceptual quality.
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4.3 High-Resolution Audio or Higher Sample Rates for Playback
High-Resolution Audio encompasses a variety of digital methodologies
and formats aimed at enhancing the encoding and playback of music by utilizing
sampling rates and bit depths that surpass the conventional 16-bit 44.1kHz
resolution commonly found in CDs, often referred to as "16/44".
There is a prevailing belief among audio experts that employing higher sampling
rates and bit depths during production results in audio of superior quality,
ultimately enhancing the listening experience for consumers. Consequently,
sampling rates such as 48kHz, 88.2kHz, 96kHz, and even 192kHz have become
common standards in the industry. Additionally, the adoption of 24-bit recording
has become widespread, offering increased workable headroom and a broader
total dynamic range in the digital domain.
However, higher sample rates do not inherently provide greater accuracy
or fidelity. In fact, the only distinction between standard sample rates lies in the
available bandwidth of our sampled signal.
Sampling at 192 kHz undeniably results in larger file sizes, which can
present challenges in terms of storage space and transmission speed.
Additionally, the computational processing speed requirements associated with
sampling at this rate are considerable, prompting inquiry into the rationale behind
its adoption.
34
Some proponents have sought to justify the use of 192 kHz sampling by
citing supposed benefits such as a narrower impulse response, suggesting
enhanced spatial localization of sonic impulses or a more analog-like behaviour.
However, such claims reveal a fundamental misunderstanding of signal theory
principles.
Bandwidth primarily concerns frequency content, while impulse response relates
to the time domain. Despite their distinct definitions, they are intrinsically linked.
Advocating for microsecond impulse response effectively promotes a megahertz
audio system, a proposition lacking practical necessity. Human auditory
capabilities do not significantly extend beyond 20 kHz, rendering frequencies
above 40 kHz largely irrelevant in terms of perceptual impact. Therefore,
arguments for excessively high sampling rates lack empirical support and fail to
align with fundamental principles of signal theory.
And remember, there is no need for higher sampling rates. There is no additional
information gained from them. Moreover, instruments produce minimal sound at
96 kHz, microphones do not respond to it, speakers do not reproduce it, and the
human ear cannot perceive it.
In addition to the absence of benefits, operating at excessive speeds
presents further disadvantages. There is an increased data size due to higher
sampling rates impacting data storage and transmission speed requirements.
Operating at 192 kHz significantly raises the demand for processing power,
resulting in costly equipment and potential compromises in audio quality.
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4.4 Higher Sample Rates Being Harmful for Playback
The theoretical capability of 192kHz audio, extending beyond 400% of the
audible limit, may initially seem promising. However, in practice, it offers
negligible benefits and can even introduce subtle compromises in fidelity.
Specifically, 192kHz digital music files fail to provide discernible advantages,
primarily due to the inclusion of ultrasonic frequencies.
These ultrasonic frequencies pose specific challenges during playback, as audio
transducers and power amplifiers inherently exhibit distortion, particularly at the
extremes of the frequency spectrum. When the same transducer reproduces
ultrasonic frequencies alongside audible content, nonlinearities can lead to the
creation of intermodulation distortion products that manifest across the entire
audible spectrum. Similarly, nonlinearities in power amplifiers can yield
comparable effects.
While these effects may be subtle, empirical listening tests have confirmed their
potential audibility, highlighting the complexities associated with high-resolution
audio formats such as 192kHz.
Figure 13: Illustration of distortion products resulting from intermodulation of a 30kHz and a
33kHz tone.
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Systems not specifically engineered to reproduce ultrasonic frequencies
typically exhibit elevated levels of distortion beyond the 20kHz threshold,
exacerbating intermodulation distortion phenomena. Expanding a system's
frequency range to accommodate ultrasonics necessitates compromises that
inevitably degrade noise and distortion performance within the audible spectrum.
Consequently,
whether
through
increased
distortion
or
compromised
performance in the audible range, the unnecessary reproduction of ultrasonic
content invariably diminishes overall system performance. Thus, prudent design
considerations are essential to strike a balance between extending frequency
response and preserving optimal performance across the audible spectrum.
Monty Montgomery (2012) presents four distinct solutions to mitigate the
additional distortion caused by ultrasonic content:
1 - Employing a dedicated ultrasonic-only speaker, amplifier, and
crossover stage to isolate and independently reproduce ultrasonic frequencies
that are inaudible to the human ear, thereby preventing interference with audible
sounds.
2 - Utilizing amplifiers and transducers specifically designed for wider
frequency reproduction to mitigate audible intermodulation caused by ultrasonics.
However, achieving this extended frequency range entails a trade-off, potentially
compromising performance in the audible portion of the spectrum.
37
3 - Implementing speakers and amplifiers meticulously engineered to
avoid reproducing ultrasonic frequencies altogether, thus circumventing the issue
of intermodulation distortion in the audible band.
4 - Opting not to encode such a wide frequency range from the outset. By
excluding ultrasonic content from the audio encoding process, the possibility of
ultrasonic intermodulation distortion within the audible band is effectively
eliminated.
While each solution offers a distinct approach, the fourth option appears
to be the most pragmatic choice, particularly concerning the goal of creating a
scalable and globally applicable solution without imposing significant resource
costs.
Therefore, careful consideration must be given to the trade-offs associated
with handling ultrasonic frequencies, weighing the potential benefits against the
costs and complexities involved. Ultimately, prioritizing improvements in the
audible range is likely to yield more tangible enhancements in overall audio
performance.
In summary, the potential audibility of intermodulation distortion resulting
from ultrasonic frequencies on a particular audio system remains subject to
variability. While in some cases, the added distortion may be imperceptible, it
could also be discernible in others, with the inclusion of ultrasonic content offering
no discernible benefits and, in many instances, visibly degrading fidelity.
Moreover, even on systems where ultrasonic content does not compromise
38
fidelity, the resources allocated to managing ultrasonics could have been
redirected towards enhancing performance within the audible range.
4.5 Oversampling
While sampling rates over 48kHz may appear irrelevant to high-fidelity
audio data, they play an essential role in several modern digital audio techniques,
with oversampling being a prime example. Unlike analog filters, digital filters have
few practical limitations, allowing for greater efficiency and precision in
completing the anti-aliasing process digitally.
In oversampling, the raw digital signal passes through a digital anti-aliasing filter
at a very high rate, ensuring effective removal of high-frequency content beyond
the Nyquist frequency. This process allows for the use of low-rate audio (e.g.,
44.1 kHz or 48 kHz) with all the fidelity benefits of higher sampling rates, such as
smooth frequency response and low aliasing, without the drawbacks of
ultrasonics that can cause intermodulation distortion and wasted space.
Moreover, it even enables us to mitigate the negative effects of linear filters like
pre-ringing by shifting them into inaudible content, ensuring a more accurate
representation of the audio signal in the audible range.
The ubiquitous integration of oversampling at remarkably high rates within
contemporary
analog-to-digital
converters
(ADCs)
and
digital-to-analog
converters (DACs) underscores its pervasive adoption and critical role in attaining
superior audio reproduction quality.
39
However, oversampling, and oversampling techniques come with
drawbacks. They can introduce latency and increase CPU usage, which can be
determinants to consider, especially in live scenarios.
Figure 14: Diagram illustrating the transition band width available for a 48kHz ADC/DAC (left)
and a 96kHz ADC/DAC (right).
4.6 Bit Depth
In digital audio, the concept of bit depth refers to the number of bits
allocated to represent each sample. Essentially, each bit can represent one of
two possible values, typically interpreted as 'on' or 'off.' Therefore, higher bit
depths permit a finer resolution in representing the amplitude of the signal.
Upon conversion of a digital signal back to analog form, regardless of the bit
depth employed during digitization, the resultant waveform appears smooth. This
phenomenon underscores the efficacy of the digital-to-analog conversion
process in faithfully reconstructing the original analog signal.
However, the journey from analog to digital introduces a crucial step:
quantization. Quantization occurs during digitization and introduces a level of
imprecision, known as quantization noise. The magnitude of this noise is
40
contingent upon the bit depth utilized during digitization. Increasing the bit depth
serves to mitigate this noise, thereby lowering the noise floor and augmenting the
dynamic range of the digital signal.
A fundamental aspect of quantization involves selecting the digital
amplitude value nearest to the original analog amplitude. While this simplistic
approach suffices in theory, its practical application often leads to undesirable
outcomes. The resulting noise from this rudimentary quantization process varies
depending on the input signal, leading to inconsistent noise characteristics or,
worse, distortion.
To address the inherent noise introduced during quantization, techniques such
as dithering are employed. Dithering involves the addition of low-level noise to
the signal, effectively masking quantization artifacts and improving the accuracy
of the digital-to-analog conversion process.
Dithering is a technique employed to mitigate the adverse effects of quantization
noise in digital audio. By adding carefully crafted low-level noise to the signal,
dithering effectively masks the undesirable artifacts associated with simple
quantization. Moreover, dithering provides producers with a degree of control
over the perceptual qualities of the introduced noise, allowing them to shape the
resultant sound according to their preferences.
Properly executed dithering serves to shape the quantization noise spectrum,
redistributing it to frequencies that are less perceptible to the human ear. This
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process results in a pristine, artifact-free audio file conducive to optimal playback
fidelity.
For instance, a standard bit depth of 16 bits yields a theoretical dynamic range of
approximately 96 dB SPL (Sound Pressure Level), with each additional bit
contributing an incremental 6 dB to the dynamic range. However, through the
judicious application of dithering techniques, the effective dynamic range
achievable with 16-bit depth can extend to approximately 120 dB SPL.
In practical music playback scenarios, the dynamic range of the audio is
typically fixed, determined by the bit depth and quality of the recording. Thus,
optimizing the bit depth and employing appropriate dithering techniques are
critical considerations in ensuring optimal audio fidelity and dynamic range in
digital audio production and playback.
In music production scenarios, the manipulation of gain and dynamic
range is a common practice aimed at achieving desired sonic characteristics.
Here, the adoption of a 24-bit depth offers distinct advantages, primarily due to
its ability to provide a lower noise floor and expanded dynamic range, thus
affording greater headroom for signal processing.
The prevalence of modern workflows emphasizes the importance of these
considerations. Contemporary production techniques facilitate extensive signal
processing, wherein numerous operations are applied to individual signals or
tracks within a composition. However, the accumulation of noise inherent in these
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processes can pose challenges, particularly when mixing multiple tracks into a
cohesive final product.
It is also worth mentioning that increasing the bit depth of the audio
representation from 16 to 24 bits does not increase the perceptible resolution or
'fineness' of the audio. It only increases the dynamic range, the range between
the softest possible and the loudest possible sound, by lowering the noise floor.
However, a 16-bit noise floor is already below what we can hear.
In summary, 16-bit depth is and will always be sufficient to capture the
dynamic range of human hearing. Therefore, the dynamic range should be
increased based on the number of processes occurring before and after delivery,
as well as the number of channels involved.
4.7 How to Actually Improve Audio Quality for the End User
One of the most straightforward ways to enhance the quality of audio for
end users is to upgrade the equipment they use to reproduce music content. By
doing so, we improve the fidelity capabilities of an equipment to fullfully represent
the fidelity of a signal.
Another critical aspect that could significantly enhance end-user audio quality is
the widespread implementation of lossless audio formats as standards in music
streaming platforms. Lossless formats ensure that audio fidelity remains intact,
avoiding any potential degradation. Despite some companies already offering
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lossless formats for music consumption, it is not yet the predominant format. This
issue is primarily due to technical limitations, notably the insufficient bandwidth to
deliver lossless formats globally while maintaining transmission speed and costeffectiveness for streaming platforms.
Finally, the most effective yet challenging approach to enhancing audio
quality remains through the production of meticulously crafted mixes and
masters. While seemingly straightforward, this endeavour requires a deep
understanding of the intricacies of music production and mastering. Mastery of
this craft comes with experience and a commitment to continuous learning. It is
essential to first grasp the theoretical underpinnings as a technician before
transcending boundaries as an artist, guided by a clear artistic vision.
5. Modern Production Workflows
5.1 Production Frameworks
In light of the preceding discussion, it is pertinent to examine how this
knowledge may be applied to refine contemporary production workflows. It is
important to note that while certain engineers, such as Lavry (2004) and Bob
Stuart (1997), propose that the optimal sample rates are 52 kHz, 58 kHz, or even
60 kHz, these will not be considered for the purpose of this study as they do not
conform to industry standards. Although the validity of their conclusions regarding
optimal sample rates is not disputed, the focus of this research is on solutions
that are reproducible and widely adaptable without compatibility issues or the
need for non-standard systems. Unless there is a shift in industry paradigm
44
recognizing the advantages of such sample rates and deeming them optimal, this
study will adhere to the sample rates traditionally recognized as standards within
the industry.
5.1.1 Recording Sample Rates and Bit Depths
In the domain of audio recording, a bit depth of 24 is recommended for
various reasons. Primarily, a 16-bit depth, although operational, provides limited
flexibility in managing the signal relative to the noise floor. By contrast, a 24-bit
depth offers an expansive dynamic range that not only maintains a considerable
distance from the noise floor at the lower spectrum but also provides a broad
safety margin at the upper end to prevent potential signal clipping. It is crucial to
recognize that signal clipping leads to distortion, which is generally irreversible.
While de-clipping processes exist, they are intended for use in exceptional
circumstances and are not reliable for routine dependence. Therefore,
establishing a robust signal level from the beginning is advisable.
Additionally, a 32-bit depth may be advantageous in particular contexts where the
audio material exhibits extensive dynamic variations. Nevertheless, the selection
of bit depth must take into account the number of tracks being recorded, as higher
bit depths yield larger file sizes. These considerations are vital in the decisionmaking process tailored to each specific recording scenario. Such meticulous
deliberation ensures the achievement of optimal recording quality while efficiently
managing data and storage demands. The dynamic range should be expanded
based on the number of processes to be applied subsequently.
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On the other hand, at this same stage, the sample rate will be determined
by the type of instrument being recorded, the number of tracks, the duration of
the recording, and its intended use afterward. All of this is to ensure maintaining
a good cost-benefit balance.
5.1.2 Production Sample Rates and Bit Depths
Modern production and mixing sessions frequently incorporate an array of
plugins, necessitating the careful selection of appropriate sample rates and bit
depths to meet technical goals. In the realm of production, most current Digital
Audio Workstations (DAWs) operate internally at a 32-bit floating point bit depth
or even higher, as exemplified by Reaper with his 64-bit floating point of internal
processing. However, if this is not the case, a minimum of 24 bits is recommended
for modern production involving a substantial number of tracks and processes.
While 32-bit remains a viable option, the cost-benefit ratio should be carefully
considered given the requirements of the project.
When determining the suitable sample rate for production, various factors
must be considered. Primarily, the intended final destination of the production
dictates the sample rate choice; for example, if the output is destined for a CD,
adherence to the standard 44.1 kHz, commonly denoted as '1644', is essential.
Conversely, if alternative formats or mediums are intended, such as higher
resolution audio or specialized content, a judicious selection of sample rates is
imperative to prevent inefficiencies in content utilization and storage capacity.
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Non-linear processing, such as distortion and heavy compression, may introduce
undesirable aliasing distortion. To mitigate this issue, two feasible approaches
are available. Firstly, one may opt to operate the project at a higher sample rate.
Alternatively, a more efficient strategy involves maintaining the project at a
maximum of 48kHz while selectively applying oversampling to plugins featuring
oversampling capabilities or utilizing tools like the 'oversampling chain' available
in Reaper. This method is generally more economical in terms of CPU usage
compared to running the entire session at a higher sample rate. Moreover, it
permits the targeted application of oversampling where necessary, allowing for
subjective evaluation of whether the aliasing distortion detracts from or enhances
the sound quality, potentially adding interest or sonic appeal. It is important to
note that aliasing distortion does not inherently signify poor sound quality.
When selecting a sample rate for production, it is imperative to consider
the nature of the material being used. Many contemporary producers utilize
samples from libraries, each with its own assigned sample rate and bit depth. It
is crucial to maintain consistency within a session by avoiding the use of samples
with disparate sample rates. Additionally, certain Digital Audio Workstations
(DAWs) may require sample rate conversion (SRC) to align samples with the
project's sample rate. Some DAWs perform SRC 'on the fly' during playback,
necessitating examination of how various sample rates are managed within a
session and the implications for exporting and CPU usage.
However, if the need arises to integrate a sample with a different sample rate into
a session (e.g., using a 44.1 kHz sample in a 48 kHz session), it is essential to
47
recognize that the upsampling process does not introduce additional highfrequency content that was not present in the original recording. Nonetheless,
there may be benefits derived from the slight increase in bandwidth, particularly
when employing equalizers, distortions, or other non-linear processing
techniques.
Another area where the consideration of different sample rates becomes
pertinent is in sound design. In this capacity, the utilization of higher sample rates
can offer distinct advantages for sound design techniques. Extreme pitch shifting
or stretching is a common practice within the realm of sound design. Operating
at higher sample rates provides increased bandwidth, which can be leveraged for
these techniques. Furthermore, when manipulating bandlimited signals through
pitch shifting, higher sample rates facilitate better preservation of high
frequencies and mitigate the risk of anti-aliasing distortion. This preservation is
attributed to the expanded frequency spectrum available at higher sample rates,
allowing for greater latitude in shifting frequencies without encountering the antialiasing filter.
Conversely, some individuals contend that recordings made at higher
sample rates, such as 96kHz, enable the retention of more high-frequency energy
when subjected to extreme stretching. This assertion suggests that higher
sample rates offer advantages in retaining the integrity of high-frequency content
during intensive manipulation of the signal.
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In summary, the recommended sample rate for general production is 48
kHz. This sample rate offers a slightly increased headroom for anti-aliasing
filtering, effectively minimizing undesirable artifacts within the audible spectrum,
such as pre-ringing, smearing, or anti-aliasing distortions. Importantly, this
advantage is achieved without imposing the heightened CPU demands or larger
file sizes associated with higher sample rates like 96 kHz or 88.1 kHz, thereby
optimizing the cost-benefit ratio.
However, in the event of opting for a sample rate of 96 kHz, it is crucial to
ensure the linearity of the system to prevent artifacts stemming from the
intermodulation of ultrasonic content. Therefore, vigilance in system calibration
and assessment is indispensable to uphold fidelity and integrity throughout the
production process. One method to assess system linearity is to generate a pure
tone at an ultrasonic frequency, such as 33 kHz, configure the audio interface to
the desired sample rate (96 kHz), and listen for any artifacts. The absence of
audible artifacts indicates system linearity, while the presence of noise or artifacts
signifies non-linearity, potentially causing intermodulation distortion.
5.2 Loudness
Another critical aspect to consider in music production is loudness. While
normalization serves as a preemptive measure to enhance user experience and
mitigate the need for individual distribution platforms to establish their own target
levels, it is important to recognize that these volume standards are subject to
change. Consequently, a song may become 'outdated' if it does not align with
49
contemporary loudness goals. In such instances, remastering the song may be
necessary to ensure conformity with current norms.
While established artists within the industry often possess the resources
to undertake such endeavors, independent artists may encounter significant
challenges in addressing this issue. Therefore, it is advisable to mix and master
a song to achieve its optimal cleanest loudness, while also remaining true to any
predetermined artistic intentions. By prioritizing clean and balanced loudness
during the mixing and mastering process, artists can mitigate the need for
frequent remastering and ensure their music remains relevant within evolving
industry standards.
To establish a mix anchor and framework for effective loudness
management, it is essential to identify the key elements within the mix that will
serve as the loudest components of the song. Typically, these elements include
the kick and snare (or their equivalents), as they constitute the loudest rhythmic
impulses and play a pivotal role in driving the rhythm and groove of the song,
rendering it danceable. Consequently, the aim is to ensure that the master limiter
is primarily triggered by these loudest rhythmic impulses, as exceeding this
threshold may lead to undesirable distortion.
By prioritizing the management of these key elements within the mix, producers
can maintain control over the overall loudness while preserving the integrity of
the audio signal. This approach facilitates the achievement of optimal loudness
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levels without compromising sound quality, ensuring a polished and professional
end result.
However, it is undeniable that certain music genres inherently exhibit loudness,
either as an intentional artistic choice to complement or amplify the message of
a song, or perhaps due to a lack of understanding by the artists who pioneered
the genre, resulting in distorted or excessively loud compositions from its
inception. This latter scenario may be exemplified in certain subgenres of
dubstep.
Whether driven by artistic intent or an adherence to specific loudness
targets, the following advice applies to producers navigating these challenges. In
such productions, there is often an unconscious tendency to exploit the 32floating point internal processing capability of Digital Audio Workstations (DAWs)
to push all tracks into the red before limiting them at the master limiter. To achieve
cleaner mixes in loud genres, professionals like Luca Pretolesi utilize techniques
such as multi-staging limiting or clipping. This method involves clipping or limiting
the mixes by small increments at different stages, providing greater control over
the signal and maximizing headroom at each stage while mitigating the distortion
caused by excessive signal pushing.
By adopting this approach, producers and engineers gain enhanced control over
the signal, allowing for the application of oversampling techniques at various
points in the mix and preventing distortion from occurring solely at the master
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limiter. Moreover, structuring the session in this manner facilitates consistent gain
staging, optimizing the use of emulation plugins, which often have a 'sweet spot.'
Furthermore, the use of Voltage-Controlled Amplifiers (VCAs) enables precise
control over the overall limited signal of the entire project or facilitates the
attainment of different loudness targets.
In contemporary music production, True Peak (TP) values are influenced
by technological limitations, particularly the bandwidth constraints inherent in
streaming platforms, which often require the use of lossy formats. It is prudent to
establish a TP level that aligns with the content and loudness of the song,
ensuring it never exceeds 0 dB to avoid distortion, unless intentional distortion is
desired and understood.
In accordance with the AESTD1008 standard, a -1 dBTP is recommended to
prevent distortion during conversion by lossy codecs. Additionally, a minimum
default value of -0.3 dBTP is advisable, as exceeding this threshold may lead to
distortion on approximately 80% of consumer-grade playback systems.
Moreover, establishing a consistent TP level helps prevent the need for individual
target levels on each distribution platform.
In such cases, tools such as Izotope's codec preview or Sonnox's equivalent can
be invaluable for assessing how the file will translate to consumer playback
environments, providing insight into potential distortion issues and allowing for
adjustments as necessary.
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Figure 15: Illustration of How Inter-Sample Peaks Occurred in the Analog Domain.
5.3 Good Practices
To ensure consistent audio quality regardless of the artistic or production
approach employed, adhering to a set of good practices is essential.
Firstly, it is advisable to deliver uncompressed or lossless audio files, bearing in
mind any specific requirements of the streaming service utilized. APPLE also
advises against upsampling files beyond their original format, as this process
does not enhance or restore information within the audio file. Additionally, refrain
from "bit-padding" or converting 16-bit files into 24-bit format, as this does not
contribute to improved audio quality.
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While some mastering engineers may choose to manage the Sample Rate
Conversion (SRC) process independently, it is recommended to deliver the
highest native sample rate available. Maintaining access to the highest-resolution
masters within streaming service systems ensures preparedness to leverage
future technological advancements and enhancements in music quality.
Utilizing True Peak Level meters is essential for accurately monitoring audio
levels, as they indicate the maximum (positive or negative) value of the signal
waveform in the continuous time domain (analog domain). Unlike quasi-peak
meters or sample-peak meters, which may miss true peaks lying between
samples, True Peak Level meters ensure that all peaks are detected.
By employing an oversampling meter compliant with BS.1770 standards, true
peaks can be accurately detected, allowing for precise monitoring of peak levels.
This enables producers and engineers to assess whether any peaks exceed the
desired true peak value, providing valuable insight into potential clipping or
distortion issues.
When considering the use of multistage limiting/clipping processes, it is
important to carefully evaluate the implications of oversampling. While
oversampling may be beneficial for limiters to ensure true peak limitation of the
signal, it can have counterproductive effects when applied to clippers.
Oversampling in limiters can help mitigate foldback aliasing in the audible range
caused by additional harmonics introduced during nonlinear processing.
54
However, oversampled clippers may produce peaks above their designated
ceiling, contradicting the intended goal of the clipping process.
Therefore, it is crucial to exercise caution and discernment when deciding
whether to implement oversampling in multistage limiting/clipping processes.
Evaluating the specific requirements and objectives of each stage of the audio
processing chain will facilitate informed decision-making and optimal results.
In addition to audio quality improvements, metadata plays a crucial role in
enhancing the user experience of audio content. Metadata refers to encoded
information that provides descriptions of digital content streams and media files,
encompassing audio, video, and photographic images. Within the realm of audio,
there exist multiple types of metadata serving different purposes.
Audio-related metadata serves to describe the content itself, providing essential
information about the audio file. On the other hand, control-related metadata
specifies how the audio should be played on a device, ensuring optimal playback
settings. Examples of control-related metadata include dynamic range control
(DRC) and loudness metadata, which enable compliant players to manage
loudness levels for various playback scenarios.
By leveraging metadata effectively, content creators can not only enhance the
organization and accessibility of their audio files but also optimize the playback
experience for users across different devices and environments.
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Loudness metadata serves a crucial role in audio playback by enabling compliant
players to normalize content loudness before it reaches the user's volume control.
This normalization process grants playback devices control over loudness levels,
ensuring a consistent listening experience across various content sources and
genres.
Dynamic Range Control metadata, on the other hand, is integrated into the
content during encoding without altering the original audio. It allows playback
devices to optionally adjust the dynamic range of the content based on factors
such as the capabilities of the playback device, ambient noise levels, or listener
preferences. For instance, listeners in noisy environments can opt for a limited
dynamic range to improve audibility, while those in quiet settings may prefer to
experience the content's full dynamic range for enhanced immersion.
6. Conclusion
Navigating the uncertainties and technical intricacies inherent in audio
production necessitates a thoughtful approach, considering a multitude of actions
and factors. With this in mind, we will succinctly outline meticulously crafted
directives aimed at defining and executing an optimal high-fidelity audio file
playback and user experience.
Firstly, determining the sample rate and bit depth of our projects is pivotal.
The recording stage is shaped by the content to be recorded, the intended use of
the material (such as CD recording), and the number of signals to be captured.
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As an initial recommendation or starting point, a sample rate of 48 kHz and a bit
depth of 24 bits are advised. However, for materials intended for extreme pitch
shifting or stretching techniques, a sample rate of 96 kHz may be preferable,
particularly if the number of signals is manageable or if the recording duration is
not extensive.
For production, mixing, and mastering, it is recommended to use a sample rate
of 48 kHz and a bit depth of at least 24 bits (though your DAW likely operates on
32-bit floating point). However, it is advisable to employ oversampling for
nonlinear processes like distortion, limiting, or heavy compression. Additionally,
always try to work with samples at the same sample rate as your session, unless
you are well acquainted with how your DAW handles these files.
To proceed, another relevant aspect is loudness. In this regard, it is
recommended to master each track to the cleanest loudness possible, rather than
strictly adhering to a target level of -14 LUFS. Loudness should be adjusted for
each song based on its composition, structure, and intent. Some songs may be
above the target level, while others may be below, but they should all be
normalized. However, since these target levels may change over time, avoid
adjusting to a temporary value. It is important to clarify that at no point are we
encouraging participation in the loudness war.
Another important aspect is true peak. Current true peak values are due to
technical limitations. With a true peak higher than -1 dBTP, you will experience
distortion due to conversion in lossy formats. However, over time and with the
57
implementation of lossless as a standard, this headroom can be increased to 0.3 dBTP (a critical point where if exceeded, 80% of home equipment will distort
the signal). If your material can adhere to the current standard of -1 dBTP, follow
it. On the other hand, if your music sounds great at high volumes, at least follow
the recommendation of -0.3 dBTP and use tools such as Izotope's codec preview
or Sonnox's equivalent for assessing how the file will translate to consumer
playback environments.
Once you have your master perfectly mastered and follow all the advice to
ensure audio quality, you will need to consider the following. Send uncompressed
or lossless formats to the highest-quality streaming platforms without upsampling,
bit-padding, or SRC. However, do not forget proper dithering with noise shaping
if necessary. Add relevant loudness and DRC metadata to enhance the user
experience. Since you are adding metadata, include information like the ISRC
that allows you to later collect royalties from your performing rights organization
if your music is played on the radio.
Finally, it is important to highlight the following: the increase in quality
provided by using an optimal sample rate by itself, without the synergy of
encompassing processes, does not have a significant audible impact. However,
as engineers striving for better sounding results, it's important to understand all
the nuances of the signal we work with. Perhaps on its own, the choice of sample
rate does not have a significant impact, but through all the stages, process
synergies, and accumulation of errors along the way, they add up and result in a
larger error that is significant. And, finally, I would like to finish with a reflection
58
from Bob Katz (2007): 'analog or digital, keep it simple or you're just compounding
various forms of errors the more you do!'
59
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8. Appendices
8.1 Appendix A: Lossless Distribution
The prevalence of lossless distribution formats is steadily increasing in
contemporary audio distribution paradigms. Lossless audio formats offer a
substantial reduction in the necessity for high maximum true peak buffers, as they
circumvent the introduction of artifacts or distortions inherent in the conversion
processes of lossy codecs.
Embracing lossless distribution methodologies serves as a strategic
imperative in averting generational loss, wherein each subsequent re-encode or
transcode precipitates a progressive degradation in audio fidelity. Even if the
initial encoding process proves transparent, subsequent iterations are
susceptible to the emergence of audible artifacts, thereby jeopardizing the
integrity and fidelity of the audio over time.
Moreover, the adoption of lossless distribution formats engenders the
preservation of high fidelity and quality over extended temporal horizons. This
preservationist ethos not only safeguards the intrinsic value of audio content but
also contributes to the enrichment of streaming platforms' catalogue, which serve
as pivotal repositories of global historical and cultural heritage.
A
8.2 Appendix B: High-Frequency Auditory Perception and its
Impact on High-End Audio Content Sampling
As previously discussed, the typical human auditory range spans from 20
Hz to 20 kHz for those with normal hearing, although this upper limit tends to
decrease with age due to physiological changes. Beyond this conventional
auditory spectrum, humans can perceive phenomena such as subsonic
frequencies produced by earthquakes, which are not typically audible.
Interestingly, there have been documented instances of individuals capable of
perceiving frequencies as high as 24 kHz. This capability was demonstrated in
studies such as 'Hearing Thresholds in Free-Field for Pure Tones above 20 kHz'
by Ashihara K. et al. (2006). In this research, auditory thresholds for pure tones
ranging from 2 kHz to 28 kHz were assessed. While no auditory thresholds were
detected for tones above 26 kHz, thresholds at 24 kHz were observed in 4 out of
15 participants, with thresholds exceeding 88 dB SPL. Moreover, thresholds were
evaluated under the masking conditions of a 20 kHz low-pass filtered noise to
ascertain whether participants were perceiving subharmonics in the lower
frequency range rather than the actual high-frequency signals.
Considering these findings, using a sampling rate of 44.1 kHz could potentially
result in the loss of extreme high-frequency content (between 20 kHz and 22
kHz). Increasing the sampling rate to 48 kHz could significantly mitigate this loss.
Despite overlaps in channel frequency responses and auditory thresholds,
typically these intersections occur at sound pressure levels over 100 dB SPL, a
significant increase from the 88 dB SPL recorded in the study. While typical
B
program materials, such as music, almost never features content beyond 20 kHz
and 100 dB SPL, the importance of these findings warrants further investigation,
although challenges remain due to the nuanced distinctions between high-end
frequency responses and auditory thresholds.
B
8.3 Appendix C: Considerations for Antialiasing Linear Phase
Filters
In the realm of digital audio, there exists a multitude of filter options for the
Antialiasing Filter of Your Signal. Among these choices is the linear phase type
filter. Phase alterations inherently introduce coloration into the audio signal, but
linear phase EQ stands out as a transparent option. As its name suggests, linear
phase EQ ensures consistency in phase across the frequency spectrum, thereby
minimizing additional coloration. It is important to note that linear phase mode
does not inherently enhance audio quality or fidelity; rather, it pertains solely to
how the filters manage phase.
However, it is essential to consider that linear phase EQ may introduce noticeable
latency, rendering it unsuitable for applications where real-time processing, such
as live performances, is crucial. Additionally, the utilization of linear phase filters
may lead to pre-ringing, which manifests as a peculiar sucking sound, particularly
noticeable on transients.
Technically, achieving a perfect linear phase necessitates the application of EQ
twice—once forward in time and once backward in time—to nullify any phase
changes. The latency incurred by linear phase EQ arises from the backward in
time component nullifying phase alterations. While all EQs exhibit post-ringing to
some extent, our auditory perception tends to be less sensitive to it. Linear phase
EQ effectively mitigates post-ringing but introduces an equal and opposite
amount of pre-ringing.
C
Pre-ringing manifests as an audible pre-echo preceding the onset of the signal's
transient or wave. The steep linear-phase filters commonly employed in digital
audio can exacerbate pre-ringing, potentially impacting arrival-time detection,
and stereo imaging. Mitigating these effects can be achieved by reducing the
steepness of the filter.
Therefore, it is advisable to utilize higher sampling rates, such as 48kHz,
if the filter is appropriately designed. However, if the filter lacks proper design,
increasing the sample rate—thus implementing oversampling—may prove
beneficial by shifting the downsides of this filter type into the audible range.
C
8.4 Appendix D: Higher Sample Rates association with
Professional Audio
Thirty years ago, ADCs and DACs did not universally incorporate
transparent oversampling techniques. During that time, certain recording
consoles operated at elevated sampling rates by solely employing analog filters.
Subsequent production and mastering processes capitalized on this high-rate
signal to mitigate any adverse effects stemming from analog filters within the
audible spectrum. Digital anti-aliasing and decimation steps, involving resampling
to lower rates for formats like CDs or DAT, were typically executed during the
final stages of mastering. This historical practice likely contributed to the
association of 96 kHz and 192 kHz with professional music production, as they
were among the sampling rates commonly utilized in this context.
D
8.5 Appendix E: Nuances of Loudness Perception
The human ear possesses the remarkable ability to consciously discern
amplitude differences of approximately 1 dB, while studies indicate subconscious
sensitivity to amplitude variances as minute as 0.2 dB. Given this remarkable
sensitivity, it is noteworthy that humans tend to universally perceive louder audio
as sounding better. Notably, a mere 0.2 dB difference is adequate to establish
this preference. Consequently, any comparison lacking careful amplitude
matching between options will likely result in the louder choice being preferred,
irrespective of whether the amplitude distinction is perceptible at a conscious
level.
E