Novelty detection. Juan Pablo Bello MPATE-GE 2623 Music Information Retrieval New York University

Transcription

1 Novelty detection Juan Pablo Bello MPATE-GE 2623 Music Information Retrieval New York University

2 Novelty detection Energy burst Find the start time (onset) of new events (notes) in the music signal. Short duration Steady state Onset: single instant chosen to mark the start of the (attack) transient. Unstability

3 Applications MIR: can be used for segmentation; one layer of rhythmic analysis (e.g. beat). Computational Musicology: see the groundbreaking work at ÖFAI (1, 2) Sound manipulation and synthesis: projects/artma/index.html Computational biology?!!

4 Difficulties extended transient (e.g. violin, flute) ambiguous events (vibrato, tremolo, glissandi) polyphonies -> (a)synchronous onsets perceptual vs physical

5 Difficulties extended transient (e.g. violin, flute) ambiguous events (vibrato, tremolo, glissandi) polyphonies -> (a)synchronous onsets perceptual vs physical

6 Difficulties extended transient (e.g. violin, flute) ambiguous events (vibrato, tremolo, glissandi) polyphonies -> (a)synchronous onsets perceptual vs physical

7 Architecture Audio signal Reduction Novelty (detection) function Detection (peak picking) Onsets

8 Time-domain Onsets: often characterized by an amplitude increase Envelope following (full-wave rectification + smoothing): E 0 (m) = 1 N N/2 X n= N/2 x(n + mh) w(n) Squaring instead of rectifying, results in the local energy: E(m) = 1 N N/2 X n= N/2 (x(n + mh)) 2 w(n)

9 Time-domain

10 Time-domain We can use the derivative of energy w.r.t. time -> sharp peaks during energy rise Detectable changes in loudness are proportional to the overall loudness of the sound. Simulates the ear s perception of loudness (Klapuri, 1999)

11 Time-domain

12 Frequency-domain Impulsive noise in time -> wide band noise in frequency More noticeable in high frequencies. Linear weighting of Energy HFC(m) = 2 N N/2 X k=0 X k (m) 2 k

13 Frequency-domain We can also measure change (flux) in spectral content (Duxbury, 02). SF(m) = 2 N Use half-wave rectification to only take energy increases into account SF R (m) = 2 N N/2 X k=0 H(x) =(x + x )/2 ( X k (m) X k (m 1) ) Use the (squared) L2 norm of the flux vector P N/2 k=0 H ( X k(m) X k (m 1) ) SF RL2 (m) = 2 N N/2 X k=0 [H ( X k (m) X k (m 1) )] 2

14 Spectral Flux

15 Phase deviation The change in instantaneous frequency in bin k can be used to detect onsets (Bello, 03) k(m 1) k(m) PD(m) = 2 N = 2 N N/2 X k=0 N/2 X k=0 00 k(m) = 2 N N/2 X k=0 k(m) k(m 1) princarg ( k (m) 2 k (m 1) + k (m 2))

16 Phase deviation This function can be improved by weighting frequency bins by their magnitude (Dixon, 06): PD W (m) = 2 N N/2 X k=0 X k (m) 00 k(m) and normalized: PD WN (m) = P N/2 k=0 X k(m) 00 k P (m) N/2 k=0 X k(m)

17 Phase deviation

18 Complex domain We can combine the spectral flux and phase deviation strategies, such that: ˆX k (m) = ˆX k (m) e j ˆk(m) where, ˆX k (m) = X k (m 1) ˆk(m) =princarg (2 k (m 1) k(m 2)) such that, CD(m) = 2 N P N/2 k=0 X k(m) ˆXk (m)

19 Complex domain As before, we can use half-wave rectification to improve the function (Dixon, 06): CD(m) = 2 N P N/2 k=0 RCD k(m) where, RCD k (m) = Xk (m) ˆXk (m) if X k (m) X k (m 1) 0 otherwise

20 Complex domain

21 Peak picking The function is post-processed to facilitate peak picking: Smoothing -> decrease jaggedness Normalization -> generalization of threshold values Thresholding -> eliminate spurious peaks

22 Peak picking Adaptive thresholding is a more robust choice, typically defined as: (m) = + f( m), m L apple m apple m + L where f is a function, e.g. the local mean or median, of the detection function; β increases the window length before the peak; and α is an offset value Peak picking reduces to selecting local maxima above the threshold

23 Peak picking

24 Comparing detection functions

25 Comparing detection functions

26 Comparing detection functions

27 Benchmarking correct c false false P = positive f - f + R = negative F = c c + f + c c + f 2PR P + R

28 References Dixon, S. Onset Detection Revisited. Proceedings of the 9th International Conference on Digital Audio Effects (DAFx06), Montreal, Canada, Collins, N. A Comparison of Sound Onset Detection Algorithms with Emphasis on Psycho-Acoustically Motivated Detection Functions. Journal of the Audio Engineering Society, Bello, J.P., Daudet, L., Abdallah, S., Duxbury, C., Davies, M. and Sandler, M.B. A tutorial on onset detection in music signals. IEEE Transactions on Speech and Audio Processing. 13(5), Part 2, pages , September, Bello, J.P., Duxbury, C., Davies, M. and Sandler, M. On the use of phase and energy for musical onset detection in the complex domain. IEEE Signal Processing Letters. 11(6), pages , June, Klapuri, A. Sound Onset Detection by Applying Psychoacoustic Knowledge. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Phoenix, Arizona, 1999.