week2 ch, 3: Feature extraction from A. Filtering audio signals B. Linear predictive coding LPC C. Cepstrum Feature extraction v 9a
Ch. 3: Feature extraction from audio signals A. Filtering B. Linear predictive coding LPC C. Cepstrum Feature extraction, v.9a 1 week2
(A)Filt Filtering Ways to find the spectral envelope Filter banks: uniform ectral energy envelo filter 1 filter filter 3 output ou output/ filter output Filter banks can also be non -uniform. freq LPC and Cepstral LPC parameters Vector quantization method to represent data more efficiently Feature extraction v 9a
(A) Filtering • Ways to find the spectral envelope – Filter banks: uniform – Filter banks can also be non-uniform – LPC and Cepstral LPC parameters • Vector quantization method to represent data more efficiently Feature extraction, v.9a 2 freq.. filter1 output filter2 output filter3 output filter4 output spectral envelop Spectral envelop energy
You can see the filter band output using windoWs-medla-player for a frame Try to look at it X Run energylll ragt windows-media-player To play music Right-click, select Visualization/bar and waves Video demo Spectral envelop Feature extraction v 9a Frequency
You can see the filter band output using windows-media-player for a frame • Try to look at it • Run – windows-media-player – To play music – Right-click, select • Visualization / bar and waves • Video Demo Feature extraction, v.9a 3 Spectral envelop Frequency energy
Speech recognition idea using 4 linear filters each bandwidth is 2, 5KHz Two sounds with two spectral Envelopes sear seoi,e.g spectra Envelop(se)"ar", Spectral envelop"ei Spectral envelope sear=ar Spectral envelope seei=ei energy energy eq 0 reg 10KHz filter 1 2 3 4 filter 1 2 3 4 10KHz Filter 2V3V4 Filter out W1 W2 3W4 out Feature extraction, v ga
Speech recognition idea using 4 linear filters, each bandwidth is 2.5KHz • Two sounds with two Spectral Envelopes SEar,SEei ,E.g. Spectral Envelop (SE) “ar”, Spectral envelop “ei” Feature extraction, v.9a 4 Spectral envelope SEar=“ar” energy energy Freq. Freq. Spectrum A Spectrum B filter 1 2 3 4 filter 1 2 3 4 v1 v2 v3 v4 w1 w2 w3 w4 Spectral envelope SEei=“ei” Filter out Filter out 10KHz 10KHz 0 0
Difference between two sounds or spectral envelopes SE Se) Difference between two sounds, E. g SEa={v1,v2,V34}=ar", SEoi=w1 e W2W3W4}=“ei a simple measure of the difference is Dist =sqrt(v1-W12+v2-W2 2+v3-W3 2+114 W4|2) Where x =magnitude of x Feature extraction v 9a
Difference between two sounds (or spectral envelopes SE SE’) • Difference between two sounds, E.g. • SEar={v1,v2,v3,v4}=“ar”, • SEei={w1,w2,w3,w4}=“ei” • A simple measure of the difference is • Dist =sqrt(|v1-w1|2+|v2-w2|2+|v3-w3|2+|v4- w4|2 ) • Where |x|=magnitude of x Feature extraction, v.9a 5