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Signal Processing Algorithms for Music, Marine Mammals …
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SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Signal Processing Algorithms forMusic, Marine Mammals and Speech
Yannis Stylianou
University of Crete, Computer Science Dept., Multimedia Informatics [email protected]
AUTH2008 June 23rd
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
1 Rhythmic Similarity of Music
2 Whales Click Detections
3 Measuring jitter
4 References
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Rhythmic Similarity of Music Based onDynamic Periodicity Warping
In collaboration with: Andre Holzapfel([email protected])
It was presented at ICASSP 2008, Las Vegas
References: [1]
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
What is used for?
Organize your huge collection of songs according to theirrhythm.
Help ethnomusicologists to categorize and reveal musicalstructure of field recordings from some country.
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Approaches to the problem
Beat spectra, cosine measure (J. Foote et al., 2002) [2]
Tempo based spectra (G. Peeters, 2005) [3]
Tactus based Rhythmic patterns (J. Paulus et al., 2002)[4]
We suggest the use of continuous periodicity spectra and awarping strategy to cope with large variations in tempo.
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Approaches to the problem
Beat spectra, cosine measure (J. Foote et al., 2002) [2]
Tempo based spectra (G. Peeters, 2005) [3]
Tactus based Rhythmic patterns (J. Paulus et al., 2002)[4]
We suggest the use of continuous periodicity spectra and awarping strategy to cope with large variations in tempo.
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Periodicity Spectra
Computation of onset strength signal, p(t) (D. Ellis,MIREX2006, beat tracking contest1)
Modeling of p(t)
p(t) =N∑
i=1
ei (t) ∗∑k∈Ki
δ(t − kT )
Periodicity Spectra:
P(f ) =
∣∣∣∣∣∣N∑
i=1
1
TEi (f )
∑k∈Ki
δ(f − k
T)
∣∣∣∣∣∣where f < 1000bpm (16.7Hz)
1www.music-ir.org/mirex2006/index.php/Audio Beat Tracking Results
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Periodicity Spectra
Computation of onset strength signal, p(t) (D. Ellis,MIREX2006, beat tracking contest1)
Modeling of p(t)
p(t) =N∑
i=1
ei (t) ∗∑k∈Ki
δ(t − kT )
Periodicity Spectra:
P(f ) =
∣∣∣∣∣∣N∑
i=1
1
TEi (f )
∑k∈Ki
δ(f − k
T)
∣∣∣∣∣∣where f < 1000bpm (16.7Hz)
1www.music-ir.org/mirex2006/index.php/Audio Beat Tracking Results
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Periodicity Spectra
Computation of onset strength signal, p(t) (D. Ellis,MIREX2006, beat tracking contest1)
Modeling of p(t)
p(t) =N∑
i=1
ei (t) ∗∑k∈Ki
δ(t − kT )
Periodicity Spectra:
P(f ) =
∣∣∣∣∣∣N∑
i=1
1
TEi (f )
∑k∈Ki
δ(f − k
T)
∣∣∣∣∣∣where f < 1000bpm (16.7Hz)
1www.music-ir.org/mirex2006/index.php/Audio Beat Tracking Results
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Example of periodicity spectra
345 6900
0.01
0.02
0.03
bpm
345 6900
0.01
0.02
0.03
0.04
bpm
Two examples of periodicity spectra of Siganos dance: Upperpanel is a faster example of that in the lower panel. Windowlength is 8s.
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Rhythm similarity based on DynamicPeriodicity Warping (DPW)
DPNORM PROJ
REFLINESIM
wDPW
ρ
Σ
S
dDPW
P1(f)
P2(f)
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Example of DPW computation
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Databases and baseline Distances
Databases:
D1: 698 songs from eight classes of ballroom dancesD2: 90 songs from six classes of Cretan dances
Baseline Distances
Cosine distance (inner product)Euclidean distanceCost of warping, (dCost) (J. Paulus et al., 2002)[4]Cosine distance after warping, dCosPost
Our measure: dDPW
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Databases and baseline Distances
Databases:
D1: 698 songs from eight classes of ballroom dancesD2: 90 songs from six classes of Cretan dances
Baseline Distances
Cosine distance (inner product)Euclidean distanceCost of warping, (dCost) (J. Paulus et al., 2002)[4]Cosine distance after warping, dCosPost
Our measure: dDPW
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Databases and baseline Distances
Databases:
D1: 698 songs from eight classes of ballroom dancesD2: 90 songs from six classes of Cretan dances
Baseline Distances
Cosine distance (inner product)Euclidean distanceCost of warping, (dCost) (J. Paulus et al., 2002)[4]Cosine distance after warping, dCosPost
Our measure: dDPW
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Databases and baseline Distances
Databases:
D1: 698 songs from eight classes of ballroom dancesD2: 90 songs from six classes of Cretan dances
Baseline Distances
Cosine distance (inner product)Euclidean distanceCost of warping, (dCost) (J. Paulus et al., 2002)[4]Cosine distance after warping, dCosPost
Our measure: dDPW
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Databases and baseline Distances
Databases:
D1: 698 songs from eight classes of ballroom dancesD2: 90 songs from six classes of Cretan dances
Baseline Distances
Cosine distance (inner product)Euclidean distanceCost of warping, (dCost) (J. Paulus et al., 2002)[4]Cosine distance after warping, dCosPost
Our measure: dDPW
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Databases and baseline Distances
Databases:
D1: 698 songs from eight classes of ballroom dancesD2: 90 songs from six classes of Cretan dances
Baseline Distances
Cosine distance (inner product)Euclidean distanceCost of warping, (dCost) (J. Paulus et al., 2002)[4]Cosine distance after warping, dCosPost
Our measure: dDPW
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Databases and baseline Distances
Databases:
D1: 698 songs from eight classes of ballroom dancesD2: 90 songs from six classes of Cretan dances
Baseline Distances
Cosine distance (inner product)Euclidean distanceCost of warping, (dCost) (J. Paulus et al., 2002)[4]Cosine distance after warping, dCosPost
Our measure: dDPW
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Databases and baseline Distances
Databases:
D1: 698 songs from eight classes of ballroom dancesD2: 90 songs from six classes of Cretan dances
Baseline Distances
Cosine distance (inner product)Euclidean distanceCost of warping, (dCost) (J. Paulus et al., 2002)[4]Cosine distance after warping, dCosPost
Our measure: dDPW
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Databases and baseline Distances
Databases:
D1: 698 songs from eight classes of ballroom dancesD2: 90 songs from six classes of Cretan dances
Baseline Distances
Cosine distance (inner product)Euclidean distanceCost of warping, (dCost) (J. Paulus et al., 2002)[4]Cosine distance after warping, dCosPost
Our measure: dDPW
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
More on Cretan dances database (D2)
Table: Tempi of D2 and Listeners’ accuracy
Dance Tempo Range (♩) Listeners’ acc. (%)
Kalamatianos 116-142 93.3
Siganos 93-103 88.9
Maleviziotis 132-160 79.2
Pentozalis 123-182 45.6
Sousta 111-136 58.3
Chaniotis 58-79 88.5
Mean 75.6
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Results on D1: Ballroom dances
Table: Classification Accuracies on D1
wkNN kNN
Cosine 85.5 (k=7) 84.5 (k=3)
Euclidean 83.8 (k=6) 82.7 (k=3)
dCost 72.4 (k=14) 70.7 (k=7)
dCosPost 70.7 (k=32) 69.2 (k=17)
dDPW 82.1 (k=11) 80.9 (k=20)
10 repetitions of 10-fold stratified cross-validation
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Results on D2: Cretan dances
Table: Classification Accuracies on D2
wkNN kNN
Cosine 53.8 (k=1) 53.8 (k=1)
Euclidean 48.9 (k=1) 48.8 (k=1)
dCost 51.8 (k=18) 48.5 (k=8)
dCosPost 51.1 (k=19) 48.7 (k=12)
dDPW 69.0 (k=4) 64.4 (k=5)
10 repetitions of 10-fold stratified cross-validation
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Whales Click detection using theTeager-Kaiser operator and PhaseSpectra
In collaboration with: Varvara Kandia([email protected])
Presented at:
ECS 2008 (The Netherlands),3rd Workshop on Detection and Classification of MarineMammals, Boston 20072nd Workshop on Detection and Classification of MarineMammals, Monaco 2006
References:[5][6][7]
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Why to do it?
Localization and tracking with passive acoustics
Study animal behavior
Abundance estimation
Correlations with physiology (size of animals, soundproduction mechanism)
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Examples of clicks from Sperm whales
Regular clicks:
0 500 1000 1500 2000 2500 3000 3500
−1
−0.5
0
0.5
1(a)
Time in ms
Am
plitu
de
Creak clicks:
0 50 100 150 200 250 300 350 400−0.04
−0.02
0
0.02
0.04(a)
Time in ms
Am
plitu
de
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Examples of clicks from Beaked whales
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Approaches/Softwares for clickdetection
Rainbow click (D. Gillespie, 1997)[8]
Moby click (O. Jake, 1996)[9]
Ishmael (D. Mellinger, 2001)[10]
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Teager-Kaiser energy operator[5][6]
Definition for a discrete time signal
Ψ[s(n)] = s2(n)− s(n + 1)s(n − 1)
For a signal with 3 components: interference x [n],transient y [n], and noise u[n], so s[n] = x [n] + y [n] + u[n]:
Ψ[s(n)] = Ψ[x(n)] + Ψ[y(n)] + Ψ[u(n)] + T [n]
we may show that:
Ψ[s(n)] ≈ Ψ[y(n)] + w(n)
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Teager-Kaiser energy operator[5][6]
Definition for a discrete time signal
Ψ[s(n)] = s2(n)− s(n + 1)s(n − 1)
For a signal with 3 components: interference x [n],transient y [n], and noise u[n], so s[n] = x [n] + y [n] + u[n]:
Ψ[s(n)] = Ψ[x(n)] + Ψ[y(n)] + Ψ[u(n)] + T [n]
we may show that:
Ψ[s(n)] ≈ Ψ[y(n)] + w(n)
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Synthetic example
0 50 100 150 200150
200
250(a)
Time (ms)
Am
plitu
de
0 50 100 150 200−0.5
0
0.5
1(b)
Time (ms)
Am
plitu
de
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Applied on clicks
From Sperm whales, Regular clicks: (a) Raw file, (b) after TK
0 500 1000 1500 2000 2500 3000 3500−1.5
−1
−0.5
0
0.5
1(a)
Time (ms)
Am
plitu
de
0 500 1000 1500 2000 2500 3000 3500−0.5
0
0.5
1(b)
Time (ms)
Am
plitu
de
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Applied on clicks
From Sperm whales, Creak clicks: (a) Raw file, (b) after TK
0 50 100 150 200 250 300 350 400−0.04
−0.02
0
0.02
0.04(a)
Time (ms)
Am
plitu
de
0 50 100 150 200 250 300 350 400
0
0.5
1(b)
Time (ms)
Am
plitu
de
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Comparison with Rainbow click
Det. Score: =Correctly detected hand labeled clicks
Total hand labeled clicks100
Table: Percentage (%) of correctly identified clicks per file.Tolerance of 2ms.
TK RBFile name clicks score (%) clicks score (%) clicks
F1 266 (0) 100 268 94.74 265
F2 944 (549) 60.17 986 15.68 781
F3 689 (414) 94.05 732 71.12 622
F4 529 (242) 99.81 528 75.05 440
F5 435 (155) 75.17 387 69.20 347
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
In terms of ROC curves
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 60
10
20
30
40
50
60
70
80
90
100Approximate ROC
Det
ectio
n R
ate
(%)
Tolerance (ms)
TKRB
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Phase Spectrum[7]
Group delay:
τ(ω) = −dφ(ω)
dωor
τ(ω) =XR(ω)YR(ω) + XI (ω)YI (ω)
|X (ω)|2
where:
X (ω) = F(x [n]) = XR(ω) + jXI (ω)Y (ω) = F(nx [n]) = YR(ω) + jYI (ω)
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Motivation
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Motivation
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Application on the Beaked whalesexample
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Zoom on in an area of clicks
After applying an appropriate modulation and low-pass filteringto the original recordings.
Note: Triangles denote hand labels
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Results on Beaked and Sperm Whales
Raw data/With TK
Species clicks Det (%) Corr (%) MAE (ms)
Beaked Whales 248 84.9/86.3 87.1/88.2 1.1/0.9
Sperm Whales 146 87.7/90.4 84.9/84.3 1.58/0.97
Det = Number of clicks correctly detectedTotal × 100
Corr = Total−Deleted−InsertedTotal × 100
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
A Mathematical Model for AccurateMeasurement of Jitter
In collaboration with: Miltiadis Vasilakis([email protected])
It was presented at MAVEBA 2007, Florence
References: [11]
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Jitter
Definition
Jitter is defined as perturbations of the glottal source signalthat occur during vowel phonation and affect the glottal pitchperiod.
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Definitions
Let u[n] be the pitch period sequence.
Local jitter
1N−1
∑N−1n=1 |u(n + 1)− u(n)|
1N
∑n=1N u(n)
Absolute jitter
1
N − 1
N−1∑n=1
|u(n + 1)− u(n)|
Relative average Perturbation
1N−2
∑N−2n=1
|2u(n+1)−u(n)−u(n+2)|3
1N
∑n=1N u(n)
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Definitions
Let u[n] be the pitch period sequence.
Local jitter
1N−1
∑N−1n=1 |u(n + 1)− u(n)|
1N
∑n=1N u(n)
Absolute jitter
1
N − 1
N−1∑n=1
|u(n + 1)− u(n)|
Relative average Perturbation
1N−2
∑N−2n=1
|2u(n+1)−u(n)−u(n+2)|3
1N
∑n=1N u(n)
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Definitions
Let u[n] be the pitch period sequence.
Local jitter
1N−1
∑N−1n=1 |u(n + 1)− u(n)|
1N
∑n=1N u(n)
Absolute jitter
1
N − 1
N−1∑n=1
|u(n + 1)− u(n)|
Relative average Perturbation
1N−2
∑N−2n=1
|2u(n+1)−u(n)−u(n+2)|3
1N
∑n=1N u(n)
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Our approach
1
ampl
itude
� ����� ��� ����� ��
time (samples)
P − ε P − εP + ε P + ε
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
In mathematical terms
We model the glottal impulse train as:
p[n] =+∞∑
k=−∞δ[n − (2k)P] +
+∞∑k=−∞
δ[n + ε− (2k + 1)P]
We may show that its power spectrum is then:
|P(ω)|2 = H(ε, ω) + S(ε, ω)
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
In mathematical terms
We model the glottal impulse train as:
p[n] =+∞∑
k=−∞δ[n − (2k)P] +
+∞∑k=−∞
δ[n + ε− (2k + 1)P]
We may show that its power spectrum is then:
|P(ω)|2 = H(ε, ω) + S(ε, ω)
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Examples of power spectrum
On synthetic glottal signal
−40
−38
−36
−34
−32
−30
−28
−26
radian frequency (ω)
pow
er (
dB)
� ����� ����� ����� ����� ������ �
H(0, ω)
S(0, ω)
H(1, ω)
S(1, ω)
H(2, ω)
S(2, ω)
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Examples of power spectrum
0 5 10 15 20−80
−70
−60
−50
−40
−30
−20harmonic & subharmonic parts of the power spectrum
frequency (kHz)
pow
er (
dB)
2.4 2.45 2.5 2.55 2.6
−55
−50
−45
a closer look at the first crossing
frequency (kHz)
pow
er (
dB)
0 5 10 15 20−80
−70
−60
−50
−40
−30
−20synthetic jitter signal (fs = 48kHz, ε = 5): power spectrum of a single frame
frequency (kHz)
pow
er (
dB)
H(ε, ω)
S(ε, ω)
|P(ω)|2
acceptedcrossing
rejectedcrossings
the circles indicatecrossings betweenthe harmonic andsubharmonic parts
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Experiments
Goal: discriminate pathological from normal voices, based onjitter
Database: Massachusetts Eye and Ear Infirmary (MEEI)[12]
Sustained vowels,53 subjects with normal voice,657 subjects with a wide variety of pathological conditions
Jitter estimation methods:
PRAAT2007 (P. Boersma and D. Weenink) [13]Multi-Dimensional Voice Program (MDVP), (Kay-Pentaxelemetrics, 2007) [14]Our approach [11]
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Experiments
Goal: discriminate pathological from normal voices, based onjitter
Database: Massachusetts Eye and Ear Infirmary (MEEI)[12]
Sustained vowels,53 subjects with normal voice,657 subjects with a wide variety of pathological conditions
Jitter estimation methods:
PRAAT2007 (P. Boersma and D. Weenink) [13]Multi-Dimensional Voice Program (MDVP), (Kay-Pentaxelemetrics, 2007) [14]Our approach [11]
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
Results in ROC curves
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
False Positive Rate
Tru
e P
ositi
ve R
ate
MDVP JitaProposed method, fixed frame, sequence averageProposed method, variable frame, sequence averagePraat Jitter (local, absolute)
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
A. Holzapfel and Y. Stylianou.
Rhythmic similarity of music based on dynamic periodicity warping.In IEEE ICASSP 2008.
Jonathan Foote, Matthew D. Cooper, and Unjung Nam.
Audio retrieval by rhythmic similarity.In Proc. of ISMIR 2002 - 3rd International Conference on Music Information Retrieval, 2002.
Geoffroy Peeters.
Rhythm classification using spectral rhythm patterns.In Proc. of ISMIR 2005 - 6th International Conference on Music Information Retrieval, pages644–647, 2005.
Jouni Paulus and A.P. Klapuri.
Measuring the similarity of rhythmic patterns.In Proc. of ISMIR 2002 - 3rd International Conference on Music Information Retrieval, 2002.
V. Kandia and Y. Stylianou.
Detection of creak clicks of sperm whales in low SNR conditions.In CD Proc. IEEE Oceans, Brest, France, 2005.
V. Kandia and Y. Stylianou.
Detection of sperm whale clicks based on the Teager-Kaiser energy operator.Applied Acoustics, 67(11-12):1144–1163, 2006.
V. Kandia and Y. Stylianou.
Detection of clicks based on group delay.Accepted in Canadian Acoustics, 2008.
D. Gillespie.
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References
An acoustic survey for sperm whales in the Southern Ocean sanctuary conducted from the R/VAurora Australis.Rep. Int. Whal. Comm., 47:897–908, 1997.
O. Jake.
Acoustic Censusing of sperm whales at Kaikoura, New Zealand: An inexpensive method to countclicks and whales automatically.Master Thesis, University of Otago, Dunedin, New Zealand, 1996.
D. K. Mellinger.
Ishmael 1.0 Users Guide.NOAA, NOAA/PMEL/OERD, 2115 SE OSU Drive, Newport, OR 97365-5258, 2001.Technical Memorandum OAR PMEL-120.
M. Vasilakis and Y. Stylianou.
A mathematical model for accurate measurement of jitter.In MAVEBA 2007, Florence, Italy, 2007.
Kay Elemetrics.
Disordered Voice Database (Version 1.03), 1994.
Paul Boersma and David Weenink.
Praat: doing phonetics by computer (Version 4.6.24) [Computer program], 2007.
Kay Elemetrics.
Multi-Dimensional Voice Program (MDVP) [Computer program], 2007.
SignalProcessingAlgorithmsfor Music,Marine
Mammals andSpeech
YannisStylianou
Outline of thetalk
RhythmicSimilarity ofMusic
Whales ClickDetections
Measuringjitter
References