Auditory Periphery: From Pinna to Auditory Nerve

Meddis, Ray; Lopez‐Poveda, Enrique A.

doi:10.1007/978-1-4419-5934-8_2

Cited by 25 publications

(14 citation statements)

References 121 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further processing stages crudely approximate the neural-transduction process in the inner hair cells by applying half-wave rectification and square-root compression to the output of each individual Gammatone filter [63]. Although not considered in this chapter, more elaborate models of the neural-transduction process might be applied at this stage [53,54,72]. Then, on the basis of these auditory signals, denoted as h L, f and h R, f , the normalized cross-correlation, C, can be computed over a window of B samples as a function of time lag, τ , frame number, t, and frequency channel, f , as follows,…”

Section: Auditory-inspired Approachesmentioning

confidence: 99%

Binaural Localization and Detection of Speakers in Complex Acoustic Scenes

May

Par

Kohlrausch

2013

The Technology of Binaural Listening

View full text Add to dashboard Cite

Section: Auditory-inspired Approachesmentioning

confidence: 99%

Binaural Localization and Detection of Speakers in Complex Acoustic Scenes

May

Par

Kohlrausch

2013

The Technology of Binaural Listening

View full text Add to dashboard Cite

“…They are relatively simple to implement. A bank of parallel GT filters with overlapping passbands can be used to approximate the basilar membrane's (BM) functionality, thereby facilitating simulation of the excitation pattern of the whole cochlear partition [11,22,27].…”

Section: Gammatone Filterbankmentioning

confidence: 99%

“…The GT filters used here are designed using the Auditory Toolbox developed by Malcolm Slaney, based on Roy Patterson's cochlear model [11,27]. In Patterson's model, each cochlear filter's bandwidth is prescribed by an Equivalent Rectangular Bandwidth (ERB), which provides a psychoacoustic measure of the auditory filter's width at any point along the cochlea [22].…”

Section: Current Implementation and Simulationmentioning

confidence: 99%

Improved Auditory-Inspired Signal Processing Algorithm Design for Tracking Multiple Frequency Components

2020

View full text Add to dashboard Cite

An adaptive synchrony capture filterbank (SCFB) signal processing architecture for tracking signal frequency components was proposed in a previous paper. Detailed analysis and further improvements are presented here. The original SCFB signal processing architecture consisted of a fixed array of traditional, passive linear, gammatone filters in cascade with a bank of three adaptively tunable bandpass filters that form part of a frequency discriminator loop (FDL). The SCFB exhibits many desirable properties for processing speech, music, and other complex sounds. In this article, an improved SCFB architecture is presented along with detailed specification of parameters that yield better frequency tracking. This architecture combines a FDL with a phase-locked loop, a concept borrowed from communication systems, to acquire and track frequency components in a time-varying signal. This modified algorithm estimates frequencies with higher accuracy even in the presence of closely spaced tones in the input. The improved performance of the algorithm is demonstrated by comparing the mean square error in the frequency estimates of synthetic time-varying signals.

show abstract

“…Mel-frequency features were extracted by calculating log Mel-frequency magnitude. Nonlinear auditory filters [33] with 128 channels were utilized to extract nonlinear auditory features. First, 512-cepstral coefficients were extracted as cepstral features.…”

Section: Classification Experimentsmentioning

confidence: 99%

Auditory Inspired Convolutional Neural Networks for Ship Type Classification with Raw Hydrophone Data

Shen

Yang

et al. 2018

Entropy

View full text Add to dashboard Cite

Detecting and classifying ships based on radiated noise provide practical guidelines for the reduction of underwater noise footprint of shipping. In this paper, the detection and classification are implemented by auditory inspired convolutional neural networks trained from raw underwater acoustic signal. The proposed model includes three parts. The first part is performed by a multi-scale 1D time convolutional layer initialized by auditory filter banks. Signals are decomposed into frequency components by convolution operation. In the second part, the decomposed signals are converted into frequency domain by permute layer and energy pooling layer to form frequency distribution in auditory cortex. Then, 2D frequency convolutional layers are applied to discover spectro-temporal patterns, as well as preserve locality and reduce spectral variations in ship noise. In the third part, the whole model is optimized with an objective function of classification to obtain appropriate auditory filters and feature representations that are correlative with ship categories. The optimization reflects the plasticity of auditory system. Experiments on five ship types and background noise show that the proposed approach achieved an overall classification accuracy of 79.2%, which improved by 6% compared to conventional approaches. Auditory filter banks were adaptive in shape to improve accuracy of classification.

show abstract

Auditory Periphery: From Pinna to Auditory Nerve

Cited by 25 publications

References 121 publications

Binaural Localization and Detection of Speakers in Complex Acoustic Scenes

Binaural Localization and Detection of Speakers in Complex Acoustic Scenes

Improved Auditory-Inspired Signal Processing Algorithm Design for Tracking Multiple Frequency Components

Auditory Inspired Convolutional Neural Networks for Ship Type Classification with Raw Hydrophone Data

Contact Info

Product

Resources

About