Extracting Information
from Music Audio
Dan Ellis
Laboratory for Recognition and Organization of Speech and Audio
Dept. Electrical Engineering, Columbia University, NY USA
http://labrosa.ee.columbia.edu/
1.
2.
3.
4.
Learning Music
Melody Extraction
Drum Pattern Modeling
Music Similarity
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LabROSA Overview
Information
Extraction
Music Eigenrhythms
Machine Meeting
Learning turns
Environment
Personal
audio
FDLP Signal
Processing
Speech
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1. Learning from Music
• A lot of music data available
e.g. 60G of MP3
≈ 1000 hr of audio, 15k tracks
• What can we do with it?
implicit definition of ‘music’
• Quality vs. quantity
Speech recognition lesson:
10x data, 1/10th annotation, twice as useful
• Motivating Applications
music similarity / classification
computer (assisted) music generation
insight into music
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Ground Truth Data
File: /Users/dpwe/projects/aclass/aimee.wav
music data available
manual annotation is
much rarer
7000
6500
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t
0:02
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f 9 Printed: Tue Mar 11 13:04:28
• A lot of unlabeled
Hz
0:06
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• Unsupervised structure discovery possible
.. but labels help to indicate what you want
• Weak annotation sources
mus
artist-level descriptions
symbol sequences without timing (MIDI)
errorful transcripts
• Evaluation requires ground truth
limiting factor in Music IR evaluations?
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0:14
vox
0:16
0:18
mu
Talk Roadmap
4
Similarity/
recommend'n
Anchor
models
Music
audio
1
Semantic
bases
Melody
extraction
Drums
extraction
2
3
Event
extraction
Music Info Extraction - Ellis
Fragment
clustering
Eigenrhythms
Synthesis/
generation
?
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2. Melody Transcription
with Graham Poliner
• Audio → Score very desirable
for data compression, searching, learning
• Full solution is elusive
signal separation of overlapping voices
music constructed to frustrate!
• Simplified problem:
“Dominant Melody” at each time frame
Frequency
4000
3000
2000
1000
0
0
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Music Info Extraction - Ellis
4.5
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Time
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Break tracks
- need to detect new ‘onset’
at single frequencies
500
time / s
Conventional Transcription
0.06
0.04
0.02
0
0
0
0
1
2
3
4
• Pitched notes have harmonic spectra
0.5
→1 transcribe
by searching for harmonics
1.5 time / s
ram
Modeling
e.g. sinusoid modeling + grouping
Group by onset &
freq / Hz
common 3000
harmonicity
find sets2500
of tracks that start
ut-signal
around 2000
the same time
1500
- + stable1000
harmonic pattern
500
Pass on to 0constraint‘onset’
0
1
2
3
based
filtering...
es
APR - Dan Ellis
e/s
4
time / s
• Explicit expert-derived knowledge
L10 - Music Analysis
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Transcription as Classification
• Signal models typically used for transcription
harmonic spectrum, superposition
• But ... trade domain knowledge for data
transcription as pure classification problem:
Audio
Trained
classifier
p("C0"|Audio)
p("C#0"|Audio)
p("D0"|Audio)
p("D#0"|Audio)
p("E0"|Audio)
p("F0"|Audio)
single N-way discrimination for “melody”
per-note classifiers for polyphonic transcription
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Training Data
• Need {data, label} pairs for classifier training
• Sources:
freq / kHz
pre-mixing multitrack recordings + hand-labeling?
synthetic music (MIDI) + forced-alignment?
2
1.5
1
0.5
30
0
20
2
10
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0
1
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0
0
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1
Music Info Extraction - Ellis
1.5
2
2.5
3
3.5
time / sec
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Melody Transcription Results
• Trained on 17 examples
.. plus transpositions out to +/- 6 semitones
SMO SVM (Weka)
• Tested on ISMIR MIREX 2005 set
includes foreground/background detection
Example...
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Melody Clustering
• Goal: Find ‘fragments’ that recur in melodies
.. across large music database
.. trade data for model sophistication
Training
data
Melody
extraction
5 second
fragments
VQ
clustering
Top
clusters
• Data sources
pitch tracker, or MIDI training data
• Melody fragment representation
DCT(1:20) - removes average, smoothes detail
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Melody clustering results
• Clusters match underlying contour:
• Some interesting
matches:
e.g. Pink + Nsync
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3. Eigenrhythms: Drum Pattern Space
with John Arroyo
• Pop songs built on repeating “drum loop”
variations on a few bass, snare, hi-hat patterns
• Eigen-analysis (or ...) to capture variations?
by analyzing lots of (MIDI) data, or from audio
• Applications
music categorization
“beat box” synthesis
insight
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Aligning the Data
• Need to align patterns prior to modeling...
tempo (stretch):
by inferring BPM &
normalizing
downbeat (shift):
correlate against
‘mean’ template
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Eigenrhythms (PCA)
• Need 20+ Eigenvectors for good coverage
of 100 training patterns (1200 dims)
• Eigenrhythms both add and subtract
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Posirhythms (NMF)
Posirhythm 1
Posirhythm 2
HH
HH
SN
SN
BD
BD
Posirhythm 3
Posirhythm 4
HH
HH
SN
SN
BD
BD
0.1
Posirhythm 5
Posirhythm 6
HH
HH
SN
SN
BD
BD
0
1
50
100
2
150
200
3
250
300
4
350
400
0
-0.1
0
1
50
100
2
150
200
3
250
300
4
• Nonnegative: only adds beat-weight
• Capturing some structure
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350
samples (@ 2
beats (@ 120
Eigenrhythms for Classification
• Projections in Eigenspace / LDA space
PCA(1,2) projection (16% corr)
LDA(1,2) projection (33% corr)
10
6
blues
4
country
disco
hiphop
2
house
newwave
rock 0
pop
punk
-2
rnb
5
0
-5
-10
-20
-10
0
10
-4
-8
-6
-4
-2
• 10-way Genre classification (nearest nbr):
PCA3: 20% correct
LDA4: 36% correct
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0
2
Eigenrhythm BeatBox
• Resynthesize rhythms from eigen-space
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4. Music Similarity
• Can we predict which songs
with Mike Mandel
and Adam Berenzweig
“sound alike” to a listener?
.. based on the audio waveforms?
many aspects to subjective similarity
• Applications
query-by-example
automatic playlist generation
discovering new music
• Problems
the right representation
modeling individual similarity
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Timbral Music Similarity
• Measure similarity of feature distribution
i.e. collapse across time to get density p(xi)
compare by e.g. KL divergence
• e.g. Artist Identification
learn artist model p(xi | artist X) (e.g. as GMM)
classify unknown song to closest model
Training
Artist 1
MFCCs
GMMs
KL
Artist 2
Min
Artist
KL
Test Song
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“Anchor Space”
• Acoustic features describe each song
.. but from a signal, not a perceptual, perspective
.. and not the differences between songs
• Use genre classifiers to define new space
prototype genres are “anchors”
n-dimensional
vector in "Anchor
Space"
Anchor
Anchor
Audio
Input
(Class i)
p(a1|x)
AnchorAnchor
Anchor
Audio
Input
(Class j)
|x)
p(a2n-dimensional
vector in "Anchor
Space"
GMM
Modeling
Similarity
Computation
p(a1|x)p(an|x)
p(a2|x)
Anchor
Conversion to Anchorspace
GMM
Modeling
KL-d, EMD, etc.
p(an|x)
Conversion to Anchorspace
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Anchor Space
• Frame-by-frame high-level categorizations
0
0.6
0.4
0.2
Electronica
fifth cepstral coef
compare to
raw features?
Anchor Space Features
Cepstral Features
0
0.2
0.4
0.6
madonna
bowie
0.8
1
0.5
0
third cepstral coef
5
10
15
madonna
bowie
15
0.5
properties in distributions? dynamics?
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Country
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5
‘Playola’ Similarity Browser
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Ground-truth data
• Hard to evaluate Playola’s ‘accuracy’
user tests...
ground truth?
• “Musicseer” online survey:
ran for 9 months in 2002
> 1,000 users, > 20k judgments
http://labrosa.ee.columbia.edu/
projects/musicsim/
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Evaluation
• Compare Classifier measures against
Musicseer subjective results
“triplet” agreement percentage
Top-N ranking agreement score:
! " 13
1
αr =
2
N
si = ∑ αrrαkcr
r=1
αc = α2r
First-place agreement percentage
Top rank agreement
test
- simple significance
80
70
60
SrvKnw 4789x3.58
%
50
SrvAll 6178x8.93
40
GamKnw 7410x3.96
30
GamAll 7421x8.92
20
10
0
cei
cmb
erd
Music Info Extraction - Ellis
e3d
opn
kn2
rnd
ANK
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Using SVMs for Artist ID
• Support Vector Machines (SVMs) find
hyperplanes in a high-dimensional space
relies only on matrix of
distances between points
much ‘smarter’ than
nearest-neighbor/overlap
want diversity of reference
vectors...
(w  x) + b = + 1
(w x) + b = –1
x1
x2
yi = – 1
y i = +1
w
(w x) + b = 0
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Song-Level SVM Artist ID
• Instead of one model per artist/genre,
use every training song as an ‘anchor’
then SVM finds best support for each artist
Training
MFCCs
Song Features
Artist 1
D
D
D
Artist
DAG SVM
Artist 2
D
D
D
Test Song
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Artist ID Results
• ISMIR/MIREX 2005 also evaluated Artist ID
• 148 artists, 1800 files (split train/test)
from ‘uspop2002’
• Song-level SVM clearly dominates
using only MFCCs!
e 4: Results of the formal MIREX 2005 Audio Artist ID evaluation (USPOP2002) from http://www.musicMIREX 05 Audio Artist (USPOP2002)
/evaluation/mirex-results/audio-artist/.
Rank
1
2
3
4
5
6
7
Participant
Mandel
Bergstra
Pampalk
West
Tzanetakis
Logan
Lidy
Raw Accuracy Normalized
68.3%
68.0%
59.9%
60.9%
56.2%
56.0%
41.0%
41.0%
28.6%
28.5%
14.8%
14.8%
Did not complete
Music Info Extraction - Ellis
References
-Julien Aucouturier and Francois Pachet. Improving
Runtime / s
10240
86400
4321
26871
2443
?
2005-10-26 p. 28/30
John C. Platt, Nello Cristianini, and John Shawe-Ta
Large margin dags for multiclass classification. In
Playlist Generation
• SVMs are well suited to “active learning”
solicit labels on items closest to current boundary
• Automatic player
with “skip”
= Ground truth
data collection
active-SVM
automatic playlist
generation
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Conclusions
Similarity/
recommend'n
Anchor
models
Semantic
bases
Music
audio
Melody
extraction
Drums
extraction
Event
extraction
Fragment
clustering
Synthesis/
generation
Eigenrhythms
?
• Lots of data
+ noisy transcription
+ weak clustering
musical insights?
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