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
1977 ◽  
Vol 62 (3) ◽  
pp. 649-664 ◽  
Author(s):  
Roy D. Patterson ◽  
G. Bruce Henning

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

2011 ◽  
Vol 34 (3) ◽  
pp. 419-441 ◽  
Author(s):  
FRANÇOISE BROSSEAU-LAPRÉ ◽  
SUSAN RVACHEW ◽  
MEGHAN CLAYARDS ◽  
DANIEL DICKSON

ABSTRACTEnglish-speakers' learning of a French vowel contrast (/ə/–/ø/) was examined under six different stimulus conditions in which contrastive and noncontrastive stimulus dimensions were varied orthogonally to each other. The distribution of contrastive cues was varied across training conditions to create single prototype, variable far (from the category boundary), and variable close (to the boundary) conditions, each in a single talker or a multiple talker version. The control condition involved identification of gender appropriate grammatical elements. Pre- and posttraining measures of vowel perception and production were obtained from each participant. When assessing pre- to posttraining changes in the slope of the identification functions, statistically significant training effects were observed in the multiple voice far and multiple voice close conditions.


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

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 990 ◽  
Author(s):  
Sheng Shen ◽  
Honghui Yang ◽  
Junhao Li ◽  
Guanghui Xu ◽  
Meiping Sheng

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.


2013 ◽  
Vol 765-767 ◽  
pp. 2862-2865
Author(s):  
Jin Lun Chen

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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