NDLI: Automatic feature-finding for time-frequency distributions

Content Provider	IEEE Xplore Digital Library
Author	Atlas, L. Owsley, L. McLaughlin, J. Bernard, G.
Copyright Year	1996
Description	Author affiliation: Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA (Atlas, L.)
Abstract	Given the detailed time and frequency resolution of time-frequency distributions, trainable automatic classifiers can easily be overwhelmed by the complexity of this input representation. This problem becomes even more severe as more advanced and higher resolution time-frequency distributions come into use. Our research is directed to making a better match to automatic classification by automatically finding a set of lower-dimensionality features within time-frequency distributions. We show the efficacy and generality of this approach to a wide variety of time-frequency distributions. A connection is also made to hidden Markov model-based classification and a comparative study is shown for this type of classifier for conventional and more advanced proper time-frequency distributions. We conclude that, when used within the context of hidden Markov model-based classification, the proper time-frequency distribution offers the best ability to reserve classes representing changes in constituents of short acoustic transients. We have developed a vector quantization technique which is a modified version of Kohonen's (1990) self-organizing feature map and then applied it to conventional time-frequency representations (the magnitude of the short-time Fourier transform), more advanced time-frequency representations (the minimum cross-entropy (MCE) proper and positive distribution), and to a proper-distribution derived measure.
Starting Page	333
Ending Page	336
File Size	979457
Page Count	4
File Format	PDF
ISBN	0780335120
DOI	10.1109/TFSA.1996.547481
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-06-18
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time frequency analysis Hidden Markov models Signal resolution Fourier transforms Smoothing methods Vector quantization Organizing Signal analysis Acoustic signal detection Neural networks
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Resolution analysis of the T-class time-frequency distributions

A hidden Markov model for acoustic syllabification and phoneme class labelling in continuous speech

Automatic classification of acoustic sequences by multiresolution image processing and neural networks

Structural representation of speech for phonetic classification

Real-time automatic speech recognition using HMM and neural networks

Multi-stream hidden Markov models and wavelets for vocal tract disease detection and pronunciation correction

Offline handwritten Farsi cursive text recognition using hidden Markov models

Fuzzy neural network for phoneme sequence recognition

Zero-tracking time-frequency distributions

Automatic feature-finding for time-frequency distributions

Similar Documents

Resolution analysis of the T-class time-frequency distributions

A hidden Markov model for acoustic syllabification and phoneme class labelling in continuous speech

Automatic classification of acoustic sequences by multiresolution image processing and neural networks

Structural representation of speech for phonetic classification

Real-time automatic speech recognition using HMM and neural networks

Multi-stream hidden Markov models and wavelets for vocal tract disease detection and pronunciation correction

Offline handwritten Farsi cursive text recognition using hidden Markov models

Fuzzy neural network for phoneme sequence recognition

Zero-tracking time-frequency distributions

Automatic feature-finding for time-frequency distributions