Modulation and Top-Down Processing in Audition
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1 Modulation and Top-Down Processing in Audition Malcolm Slaney 1,2 and Greg Sell 2 1 Yahoo! Research 2 Stanford CCRMA
2 Outline The Non-Linear Cochlea Correlogram Pitch Modulation and Demodulation Information Flow CASA Relational Networks
3 Cochlear Nonlinearity
4 Hair Cell Motility Cillia Pipette (changing membrane potential) By Jonathan Ashmore -
5 Auditory Nerve Patterns Frequency Time
6
7 Correlogram Architecture
8 Tone Correlograms
9 Tone Examples Distance down cochlea Center Frequency Time Interval (s) Autocorrelation Lag With help from Richard O. Duda
10 Speech Correlogram
11 Speech Examples Speech Examples Leonardo at Apple
12
13 Pitch Integrated Correlogram Narrowed Autocorrelation Pitch Peak Time Lag (ms)
14 Pitch Architecture
15 Pitch in Noise
16 Ambiguity of Pitch
17 Pitch Theories A Correlogram Temporal implementations with neural delay lines T Lag ime F requency Cochlea Sound Inpu t B Perceived Pitches Matching result Central Pattern Match Theory Templates.25 Input Spectrum CF Hz 315 Hz 175 Hz
18 Correlogram Implementation Today FFT Licklider Neural Delay Patterson Strobedtemporal integration Alternative Oscillators AM Modulator
19 Processing Chain One Dimensional (waveform) Pressure Time Two Dimensional (not a spectrogram) Cochlear Place Time Cochlear Processing Three Dimensional (neural movie) Cochlear Place Correlogram Processing Autocorrelation Lag
20 What is Modulation?
21 Applications of Demodulation Cochlear implants Chimaeric Speech Speech Enhancement Source Separation Modulation Filtering
22 Chimeara Speech
23 Demodulation Ambiguity
24 Ambiguity Example
25 Ambiguity Example
26 Goals Ability to select desired solution Flexibility to define desired solution Lower-frequency modulation (Amplitude) Higher-frequency carrier (Pitch) Handle harmonic/noisy carriers Satisfy projection property
27 Past Work Hilbert Envelope Half/Full-wave Rectifier Modulation Spectrogram S. Greenberg and B. E. D. Kingsbury. The Modulation Spectrogram: In pursuit of an invariant representation of speech. In Proceedings of the IEEE Conference on Acoustics, Speech and Signal Processing, pages , Cortical Sound Representation M. Elihilali, T. Chi, and S. A. Shamma. A spectro-temporal modulation index (STMI) for assessment of speech intelligibility. Speech Communication, 41: , Subband Carrier Demodulation S. Schimmel and L. Atlas. Coherent Envelope Detection for Modulation Filtering of Speech. In Proceedings of the IEEE Conference on Acoustics, Speech and Signal Processing, pages , 2005.
28 Hilbert Envelope
29 Half/Full Wave Rectifier
30 Modulation Spectrogram
31 Cortical Sound Representation
32 Subband Carrier Demodulation
33 Subband Decomposition? Most methods use subbands Auditory system Can t handle harmonic/noisy carriers Problems Inconsistent filter-component alignment Non-stationary components Demodulation is nonlinear Changes definition of modulation Filter Q defines modulation frequency range We want an algorithm that doesn t require it
34 Effect of Subband
35 Speech Pipeline
36 Modulation or Filtering
37 Optimal Demodulation
38 Two Solutions: Log or Linear Cepstral Domain Optimize log of signal Smooth second derivative* of m(t) Sparse carrier Restore sign Linear Domain Minimize high-frequency modulator energy Subject to m(t) > c(t)
39 Simple Examples
40 Ambiguity Example
41 Increasing Modulation Frequency
42 Modulation Sign Change
43 Harmonic Carrier
44 Stochastic Carrier
45 SNR
46 Speech
47 Block Diagrams Pressure Time Cochlear Processing Cochlear Place Time Cochlear Place Correlogram Processing Autocorrelation Lag
48 Problems Continuation Tone and Noise Parliament Cough What do you hear? Waveforms? Ideas?
49 Speech Examples Wedding Sine Natural
50 Visual Context
51 What Vowel is This? Word 1 Peter Ladefoged Word 2 Word 3
52
53
54
55 McGurk
56 Speech Object Sinewave Speech Object Wedding Speech Vowel? Environment Vision Ventroloquism Vision Dots Vision McGurk Audio Locate Audio Locate Speech
57
58 Information Flow
59 ASR Three Three Three Language model for the words: one, two, three Two Two Two One One One Word model showing phonemes for the word one /w/ / / /n/ Acoustic (phoneme) model for the phoneme / / S 1 S 2 S 3
60 Streaming of Tones
61 Auditory Scene Analysis All harmonics - Oboe Even harmonics - Soprano Odd harmonics - Clarinet
62 Old plus New Principle Slide by Dan Ellis (Columbia)
63 Conventional Scene Analysis Slide by Dan Ellis (Columbia)
64 Goto CASA with MIDI MIDI Sequence
65 Barker ASR
66 Ellis Prediction Driven
67 Saliency
68 Saliency Example Time-frequency display Saliency map shows high-interest locations
69 Saliency Maps Longer tones better Missing parts salient Modulation more salient Forward masking works
70 Sound Examples Birds Calls Cows Horse Waterfall
71 Saliency Comparison Details of saliency comparison Model predictions
72 Relational Network (Simple) Patches of neurons X Z Z Each measure one quantity M X M Y M Bidirectional relations for feedback/feedforward Thanks to Rodney Douglas m Y M
73 Relational Network (example) Relational specification Input here Relational feedback Relational Feedback
74 ASR Relational Network Bidirectional links enforce phoneme/ word constraints Cochlea Delay Phone Recognizer Phone Recognizer Word Recognizer Note: We don t know how to represent delays A patch of neurons (one of N output)
75 Desired Results Relational Feedback Without With /A/ Phoneme Patch /I/ Phoneme Patch AI Word Patch IA Word Patch Phoneme Input A A A I
76 Simulation
77 Simulation 2
78 Simulation 3
79 Grossberg ART
80 Statistical Means ICA Different distributions One Microphone GMM models of distribution
81 Conventional
82 Better?
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