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NOISE-ROBUST SPECTRO-TEMPORAL ACOUSTIC SIGNATURE
RECOGNITION USING NONLINEAR HEBBIAN LEARNING
by
Bing Lu
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOMEDICAL ENGINEERING)
August 2009
Copyright 2009 Bing Lu
Object Description
| Title | Noise-robust spectro-temporal acoustic signature recognition using nonlinear Hebbian learning |
| Author | Lu, Bing |
| Author email | blu.bing@gmail.com; blu@usc.edu |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Biomedical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2009-04-15 |
| Date submitted | 2009 |
| Restricted until | Unrestricted |
| Date published | 2009-06-16 |
| Advisor (committee chair) | Berger, Theodore W. |
| Advisor (committee member) |
Dibazar, Alireza D'Argenio, David Z. Baudry, Michel |
| Abstract | How to recognize the acoustic signal of interest in open environments where many other acoustic noises exist? The efficient auditory signal processing and intelligent neural learning contribute to this remarkable ability. We propose a nonlinear Hebbian learning (NHL), with several certain novelties, to newly implement noise-robust acoustic signal recognition. The proposed learning rule processes both time and frequency features of input. The spectral analysis is realized by using auditory gammatone filterbanks. To address temporal dynamics, the network input incorporates not only the current gammatone-filtered feature vector, but also multiple past feature vectors. We refer to this established high-dimensional input as spectro-temporal representation (STR). Given STR inputs, the exact acoustic signatures of signals of interest and the composing property among signatures are generally unknown. The nonlinear Hebbian learning rule is then employed to extract representative independent signatures, and to learn the weight vectors which transform data into signature space. During learning, NHL also reduces feature dimensionality. Comparing with linear Hebbian learning (LHL) which explores the second-order moment of data, the applied NHL involves higher-order statistics of data. Therefore, NHL can capture representative components that are more statistically independent than LHL can. Besides, the nonlinear activation function of NHL can be chosen to refer to the implicit distribution of many acoustic sounds, and thus making the learning optimized in an aspect of mutual information. The advantages of the proposed NHL over other ICA algorithms (which are often used for blind source separation) are also discussed, in terms of the criterion and optimization function.; Simulation results show that the whole proposed system can more accurately recognize signals of interest than its counterparts in severely noisy circumstances. The proposed system has been used in real-world noise-independent projects. One project is detecting moving vehicles. Noise-corrupted vehicle sound is recognized while background sounds are rejected. At low SNR= 0 dB, when vehicle data is contaminated by AWGN, human voice, and bird chirp, the proposed system dramatically decreases the error rate over normally used acoustic feature extraction method MFCC by 16%, 25%, and 68%, respectively; and, by 15.3%, 20%, and 2%, over LHL (another normally used acoustic feature extraction method). Another applicable project is vehicle type identification. The proposed system achieves better performance than LHL, e.g., 40% improvement when gasoline heavy wheeled car is contaminated with AWGN at SNR= 5 dB |
| Keyword | acoustic signal recognition; biological inspiration; noise robustness; nonlinear Hebbian learning |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m2299 |
| Rights | Lu, Bing |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Lu-2898 |
| Archival file | uscthesesreloadpub_Volume55/etd-Lu-2898.pdf |
Description
| Title | Page 1 |
| Full text | NOISE-ROBUST SPECTRO-TEMPORAL ACOUSTIC SIGNATURE RECOGNITION USING NONLINEAR HEBBIAN LEARNING by Bing Lu A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (BIOMEDICAL ENGINEERING) August 2009 Copyright 2009 Bing Lu |
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