Marvin Howard
L05/Koblaz & James
KJ8
Effective Methods for Analyzing and Matching Audio
Introduction
Equipped in every computer, smart phone, and other personal technical devices is a
music library or playlist that is organized by the user to play and control digital music and
video files. Handling the task of sorting out and controlling the playlist are different
algorithms developed by companies and programmers. Most provide various options and
settings to allow users to personalize their music or videos to their liking. This technical
review will briefly discuss different software and applications and methods they employ
to match audio tracks.
Commercial Applications
There are a variety of companies that offer applications that consist of features to help
users organize similar music. Apple’s iTunes is an application that is equipped with a
feature entitled Genius designed to analyze each user’s playlist or library and compare
the similarities within the global database to produce the best mix [5]. Another popular
software that is used to match audio tracks is beaTunes. BeaTunes is the complete library
manager that stores music by the color of their sound and/or Beat Per Minute (BPM) and
mixes each file according to the volume levels [1]. Serato, the most popular DJ software
available on the market, is a computer program that analysis music on any given playlist
and sets the BPM accordingly. Users can then adjust the songs tempo by increasing or
decreasing the pitch to match other audio tracks [6].
Technology Used
To accomplish audio matching, audio files are first analyzed followed by undergoing
techniques such as; term frequency –inverse document frequency (tf-idf), harmonic
mixings, or BPM detection. In global databases, the algorithm is designed to intake this
data and use a comparison technique known as term frequency –inverse document
frequency (tf-idf) to determine how often a particular factor occurs in a single song or
library. Once this information is collected, it is then represented as a vector. The final
step in the algorithm inputs your selected song and compares it to two others from the
database (also represented as vectors), and runs an analysis as to whichever one is closer
in angle to your query vector and is cued to play next [2]. Two techniques that beaTunes
utilizes to organize the playlist are harmonic mixing and determining the (BPM) [1].
Harmonic mixing is the act of mixing between two stored tracks that are most often either
in the same key or frequency domain [3]. The harmonic keys are converted and
represented as colors allowing software to group similar music. Finally, Searto optimizes
audio signal processing which is done using Fourier analysis. This technique obtains the
energy envelope of the audio sample and will then transform it to its frequency
representation. The method involves taking a window size no smaller than 225 with a
sampling rate of 44100 of the input, and find the signal’s boundary known as the
envelope. This sample can depict the difference of lower frequencies, usually the bass
and down beat in each song, to find the BPM within an accuracy of 0.078 [4,7].
Implementation of Audio Beat Detection
Algorithms used to detect similarities or the BPM of audio files will be utilized in
internal code of the software. Users simply have the choice to have the music analyzed
automatically and grouped according to the results. Depending upon the focus of the mix,
a technique should be chosen that results in the best output and closest accuracy. For
BPM detection, various forms of FFT should be used and for general mixing harmonic
and vector representation is best suited.
References
[1] BeaTunes., “Build Better Playlists.” [online] 07 Feb. 2011. <http://www.beatunes.com/>.
(Accessed 04 February 2011).
[2] C. Mims, " How ITunes Genius Really Works," Technology Review , Vol. , no. , , June, 2 2010.
[3] C. Rauscher, Fundamentals of Spectrum Analysis, Edition of book, Germany: Rohde &
Schwarz, 2007, p.
[4] F. Isen, DSP for MATLAB™ and LabVIEW™ III: Digital Filter Design Synthesis Lectures on
Signal Processing. Vol. 3, 3rd ed., San Rafael, CA: Morgan & Claypool, 2009, p.
[5] ITunes., “Learn about the Features of ITunes 10." Apple. [online] 07 Feb. 2011.
<http://www.apple.com/itunes/features/>. (Accessed 04 February 2011).
[6] Rane Scratch Live Version SL3. Mukiltwo WA.: Rane Corporation., 2008
[7] V. Werner, " BPM Measurement of Digital Audio by Means of Beat Graphs & Ray Shooting," ,
Vol. , no. , pp. , .[]. Yellow Couch
Papers:http://werner.yellowcouch.org/Papers/bpm04/index.html#ref15. [Accessed Feburary 5,
2011]