Brain Neuroinformatics: Auditory System

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Brain NeuroinformaticsAuditory System

• February 27, 2002
• Soo-Young Lee
• Brain Science Research Center
• Korea Advanced Institute of Science Technology
• http//braintech.kaist.ac.kr bsrc.kaist.ac.kr

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Artificial Auditory System

• Based on Human Cognitive Mechanism
• Develop mathematical model and auditory chip
• Develop Continuous speech recognition system

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Auditory Pathway

• ? Improvement of Cochlea model
• Binaural hearing model
• Auditory cortex model
• Selective attention model
• Continuous-speech recognition system
• Speech-recognition chip

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Research Scopes
Auditory Pathway
 
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Masking

• Lateral Inhibition
• Recursive

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Masking using Lateral Inhibition
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Temporal Masking
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Temporal Masking Integration Model
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Time Frequency Response
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Isolated Word Recognition Rates
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Discussions Masking

• Masking Suppresses Unwanted Noisy Components in
  Signal
• Simultaneous Masking by Lateral Inhibition
• - Recognition performance was enhanced with
  MFCC model
• - Proposed model can be used with any
  auditory model
• - ZCPA has spectral masking effect
• Temporal Masking by Unilateral Inhibition
• - Unilateral inhibition using the integration
  model
• - Model resembles other feature processing
  algorithms
• - Recognition performance was enhanced with
  RASTA parameters

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Channel Compensation

• ASR in mismatched environments
• Environmental information
• Background noise, acoustic/transmission channel
• Assume environment degradation model

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Speaker-to-Microphone mapping

• Mapper train
• Where and which type of mapper should be
  deployed?

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Adaptive Noise Cancelling

• Adaptive noise cancelling
• An approach to reduce noise based on reference
  noise signals
• System output
• The LMS algorithm

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ICA-based Approach to ANC

• The difference between the LMS algorithm and the
  ICA-based approach
• Existence of the score function
• The LMS algorithm
• Decorrelate output signal from the reference
  input
• The ICA-based approach
• Make output signal independent of the reference
  input
• Independence
• Involve higher-order statistics including
  correlation
• The ICA-based approach
• Remove the noise components using higher-order
  statistics and correlation

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TDAF approach to ANC

• Normalized LMS algorithm
• Normalized ICA-based algorithm

where
where
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Experimental Results (1)

• Experiments for artificially generated i.i.d.
  signals
• SNRs of output signals for the simple simulation
  mixing filter (dB)

Signal and Noise Initial SNRs SNRs after convergence SNRs after convergence SNRs after convergence
Signal and Noise Initial SNRs LMS algorithm ICA-based approach ICA-based approach
Signal and Noise Initial SNRs LMS algorithm
Laplacian -3.0 30.9 38.0 31.3
Laplacian 10.0 30.9 38.3 31.7
Gaussian -3.0 30.6 28.7 30.3
Gaussian 10.0 30.6 28.7 30.0
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Experimental Results (2)

• Experiments for recorded signals
• Signal waveforms for the car noise and the simple
  simulation filter

Signal source
Noise source
Primary input signal
System output signal
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Experimental Results (3)

• Experiments for recorded signals
• SNRs of output signals for the measured filter

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Experimental Results (4)

• Comparison of learning curves with and without
  TDAF
• Car noise

The ICA-based approach
The LMS algorithm
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Discussion ANC

• A method to ANC based on ICA was proposed.
• The ICA-based learning rule was derived.
• The ICA-based approach
• Include higher-order statistics
• Make the output independent of the reference
  input
• The LMS algorithm
• Make the output uncorrelated to the reference
  input
• Gave better performances than the LMS algorithm
• TDAF method was applied to the ICA-based
  approach.
• Derived the normalized ICA-based learning rule
• Improved convergence rates

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Bottom-Up and Top-Down Attention
External Cue
Internal Cue
Attended Output
Classifier Output

• Bottom-Up
• - Masking
• - ICA
• Top-Down
• - MLP
• - HMM

Bottom-Up Recognition
Top-Down Expectation
Attended Input
Bottom-Up Attention
Input Features
Brain
Environment
Input Stimulus
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??? ?? ?? ????(1)

• ??? ?? ?? ????
• ??8051???? ??(???)
• 12 MHz ?? 1.93 MIPS
• ???? ?? ??(16??)
• ?? ??? ?? ?? ???
• 50 ?? ? 20 ms ?? ?? ?? ??
• ??? 95 ??

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??? ?? ?? ????(2)

• Hynix 0.35 ??
• ??? ?? 64-TQFP
• ?? ??? 128x12, 256x8, 2048x8
• AGC, A/D, D/A ??
• 12KHz, 12 bit A/D
• 12Khz, 8 bit D/A
• ??? ?? ?? ????
• ?? ?? ????
• ?? ?? ????
• ??? ?? ???? ??

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??? ?? ?? ????(3)

• CM8051 ??? ? I/O ???

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??? ?? ?? ????(4)

• ?? ?? ???

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Analogue ICA Chip

• ? ? multiplier
• ? ? current summation
• r ? learning rate

4 x 4 ICA network in one Chip
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Test Results in Waveforms

• Source Signals (s1, s2)
• two different males voice
• 16 kHz sampled
• Mixed Signals (x1, x2)
• Instantaneous mixture
• Mixing Mtx A is
• Separated Signals (o1, o2)
• Recovered original sources

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Fabricated Chip

• 2.8mm x 2.8mm
• AMS CMOS 0.6um
• 2 poly-3 metal
• Analog Digital Hybrid Process

Die Photo of a Fabricated ICA Chip