Efficient coding of natural sounds
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Efficient Coding of Natural Sounds. Grace Wang HST 722 Topic Proposal. Background. Our natural environment consists of multiple sound sources and background noises with complex harmonic and temporal structures Stationary harmonic structure (vocalizations) Nonstationary structure (crunchy)

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Efficient Coding of Natural Sounds

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Efficient coding of natural sounds

Efficient Coding of Natural Sounds

Grace Wang

HST 722 Topic Proposal


Background

Background

  • Our natural environment consists of multiple sound sources and background noises with complex harmonic and temporal structures

    • Stationary harmonic structure (vocalizations)

    • Nonstationary structure (crunchy)

  • Have our brains developed to be optimized for processing natural sounds efficiently?


Efficient coding hypothesis

Efficient coding hypothesis

  • Shannon’s model for transferring data in communication systems (info theory)

  • Barlow applied info theory to model neural behavior

    • Proposed spikes of neural populations was optimized to efficiently represent naturally occurring images and sounds

    • Efficiency = reduce redundancy to maximize independence

    • Largely consistent with early stages of visual processing

    • Is the same true for the auditory system?


Information in adjacent filters is highly redundant

Information in adjacent filters is highly redundant

  • Nearly identical statistics across filters and across sound types

  • Suggested natural sounds may be associated with bandwidth and translation invariance

from Attias and Schreiner (1997), “Temporal low-order statistics of natural sounds”


How do we remove redundancy to achieve statistically independent neural responses

How do we remove redundancy to achieve statistically independent neural responses?

from Schwartz and Simoncelli (2000), “Natural sound statistics and divisive normalization in the auditory system”


Sound pressure waveform decomposition

Sound pressure waveform decomposition

  • Differs from spectrograms by retaining the phase information

from Smith and Lewicki (2005), “Efficient coding of time-relative structure using spikes”


Time frequency distribution of optimal filter shapes

Time-frequency distribution of optimal filter shapes

  • Fourier transform may be sufficient for animal vocalizations

  • Need wavelet analysis for environmental sounds and speech

from Lewicki (2002), “Efficient coding of natural sounds”


Decompose into modulation spectra

Decompose into modulation spectra

from Singh and Theunissen (2003), “Modulation spectra of natural sounds and ethological theories of auditory processing”


Discarding a lot of energy in white noise

Discarding a lot of energy in white noise

from Singh and Theunissen (2003), “Modulation spectra of natural sounds and ethological theories of auditory processing”


Efficient coding of natural sounds

from Singh and Theunissen (2003), “Modulation spectra of natural sounds and ethological theories of auditory processing”


Efficient coding of natural sounds

from Singh and Theunissen (2003), “Modulation spectra of natural sounds and ethological theories of auditory processing”


Neural responses

Neural responses

  • Increasing selectivity along ascending pathway for songs in zebra finches

  • Optimal stimulus set in grasshoppers does not coincide completely with their natural sounds

    • Suggested neurons are optimized for behaviorally relevant sounds (Machens et al 2005)


Papers

Papers

  • Background

    • Smith and Lewicki 2005: Efficient coding

  • Discussion

    • Attias and Schreiner 1997: Demonstrate redundant representation in peripheral auditory system

    • Lewicki 2002: Fourier vs wavelet representation

    • Singh and Theunissen 2003: Modulation spectra

  • Further reading

    • Machens et al 2005: grasshoppers, behaviorally relevant sounds

    • Hsu et al 2004: zebra finches, increasing selectivity


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