Proceedings ArticleDOI
Recognition of environmental sounds
R.S. Goldhor
- Vol. 1, pp 149-152
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TLDR
The results of this study indicate that well-isolated familiar sounds can be recognized with high accuracy by applying standard statistical classification procedures to feature vectors derived from two-dimensional cepstral coefficients.Abstract:
The author describes a preliminary study designed to answer the question, 'How well can familiar environmental sounds be identified?' By familiar is meant sounds on which the recognition system has been previously trained. Environmental sounds are sounds generated by acoustic sources common in domestic, business, and out-of-doors environments. The results of this study indicate that well-isolated familiar sounds can be recognized with high accuracy by applying standard statistical classification procedures to feature vectors derived from two-dimensional cepstral coefficients. >read more
Citations
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Proceedings ArticleDOI
Computational auditory scene recognition
TL;DR: This paper addresses the problem of computational auditory scene recognition and describes methods to classify auditory scenes into predefined classes using band-energy ratio features with 1-NN classifier and Mel-frequency cepstral coefficients with Gaussian mixture models.
Journal ArticleDOI
Comparison of techniques for environmental sound recognition
Michael A. Cowling,Renate Sitte +1 more
TL;DR: A comprehensive comparative study of artificial neural networks, learning vector quantization and dynamic time warping classification techniques combined with stationary/non-stationary feature extraction for environmental sound recognition shows 70% recognition using mel frequency cepstral coefficients or continuous wavelet transform with dynamic time Warping.
Proceedings ArticleDOI
Automatic surveillance of the acoustic activity in our living environment
TL;DR: In this paper, the authors report an experiment with an acoustic surveillance system comprised of a computer and microphone situated in a typical office environment, which continuously analyzes the acoustic activity at the recording site, separates all interesting events, and stores them in a database.
Journal ArticleDOI
Using One-Class SVMs and Wavelets for Audio Surveillance
TL;DR: 1-SVM-based multiclass classification approach overperforms the conventional hidden Markov model-based system in the experiments conducted, the improvement in the error rate can reach 50%.
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