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Harmonic Training and the Formation of Pitch Representation in a Neural Network Model of the Auditory Brain
by
Higgins, Irina
, Walker, Kerry M. M.
, Stringer, Simon M.
, Ahmad, Nasir
in
Adaptation
/ auditory brain
/ Cochlea
/ Competitive neural network
/ Fourier transforms
/ Frequency
/ Harmonic Training
/ Neural networks
/ Neuroscience
/ Noise
/ pitch identification
/ Sound
/ Theory
/ unsupervised learning
2016
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Harmonic Training and the Formation of Pitch Representation in a Neural Network Model of the Auditory Brain
by
Higgins, Irina
, Walker, Kerry M. M.
, Stringer, Simon M.
, Ahmad, Nasir
in
Adaptation
/ auditory brain
/ Cochlea
/ Competitive neural network
/ Fourier transforms
/ Frequency
/ Harmonic Training
/ Neural networks
/ Neuroscience
/ Noise
/ pitch identification
/ Sound
/ Theory
/ unsupervised learning
2016
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Do you wish to request the book?
Harmonic Training and the Formation of Pitch Representation in a Neural Network Model of the Auditory Brain
by
Higgins, Irina
, Walker, Kerry M. M.
, Stringer, Simon M.
, Ahmad, Nasir
in
Adaptation
/ auditory brain
/ Cochlea
/ Competitive neural network
/ Fourier transforms
/ Frequency
/ Harmonic Training
/ Neural networks
/ Neuroscience
/ Noise
/ pitch identification
/ Sound
/ Theory
/ unsupervised learning
2016
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Harmonic Training and the Formation of Pitch Representation in a Neural Network Model of the Auditory Brain
Journal Article
Harmonic Training and the Formation of Pitch Representation in a Neural Network Model of the Auditory Brain
2016
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Overview
Attempting to explain the perceptual qualities of pitch has proven to be, and remains, a difficult problem. The wide range of sounds which elicit pitch and a lack of agreement across neurophysiological studies on how pitch is encoded by the brain have made this attempt more difficult. In describing the potential neural mechanisms by which pitch may be processed, a number of neural networks have been proposed and implemented. However, no unsupervised neural networks with biologically accurate cochlear inputs have yet been demonstrated. This paper proposes a simple system in which pitch representing neurons are produced in a biologically plausible setting. Purely unsupervised regimes of neural network learning are implemented and these prove to be sufficient in identifying the pitch of sounds with a variety of spectral profiles, including sounds with missing fundamental frequencies and iterated rippled noises.
Publisher
Frontiers Research Foundation,Frontiers Media S.A
Subject
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