Research on the calculation of SNR of audio based on auditory filter

2009 ◽  
Author(s):  
Chenglin Shi ◽  
Xiaoqing Yu ◽  
Wanggen Wan
Keyword(s):  
Auditory Filter

scholarly journals Estimate of auditory filter shape using notched-noise masking for various signal frequencies

2006 ◽  
Vol 27 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Masashi Unoki ◽  
Kazuhito Ito ◽  
Yuichi Ishimoto ◽  
Chin-Tuan Tan
Keyword(s):  
Noise Masking ◽  
Auditory Filter

scholarly journals Effect of stimulus variability on auditory filter shape

1976 ◽  
Vol 60 (S1) ◽  
pp. S116-S116
Author(s):  
Roy D. Patterson ◽  
G. Bruce Henning
Keyword(s):  
Auditory Filter ◽  
Stimulus Variability

scholarly journals Psychophysical auditory filter estimates reveal sharper cochlear tuning in musicians

2014 ◽  
Vol 136 (1) ◽  
pp. EL33-EL39 ◽  
Author(s):  
Gavin M. Bidelman ◽  
Jonathan M. Schug ◽  
Skyler G. Jennings ◽  
Shaum P. Bhagat
Keyword(s):  
Auditory Filter ◽  
Cochlear Tuning

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

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 990 ◽  
Author(s):  
Sheng Shen ◽  
Honghui Yang ◽  
Junhao Li ◽  
Guanghui Xu ◽  
Meiping Sheng
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Filter Banks ◽  
Auditory Filter ◽  
Feature Representations ◽  
Proposed Model ◽  
Energy Pooling ◽  
Practical Guidelines ◽  
Frequency Components ◽  
Underwater Acoustic Signal

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.

DSP-Based Implementation and Optimization of Auditory Filter-Bank

2013 ◽  
Vol 765-767 ◽  
pp. 2862-2865
Author(s):  
Jin Lun Chen
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Filter Bank ◽  
Audio Signal ◽  
Audio Signal Processing ◽  
Auditory Model ◽  
Auditory Filter ◽  
The Real ◽  
Model Based ◽  
Signal Processing Systems

The auditory filter-bank is the key component of auditory model, and its implementation involves a lot of computations. The time spent by an auditory filter-bank to finish its work has a significant effect on the real-time implementation of auditory model-based audio signal processing systems. In this paper, we give a brief introduction to the auditory filter-bank at the first, and then discuss its DSP-based implementation and optimization in details.

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