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Environmental sounds recognition system using the speech recognition system techniques

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3 Author(s)
Uribe, O.A. ; ESIME-IPN, Mexico City, Mexico ; Meana, H.M.P. ; Miyatake, M.N.

This paper proposes an environmental sounds recognition system using LPC-cepstral coefficients for characterization and a backpropagation artificial neural network as verification method. LPC-cepstral data are totally dependent on the sound-source from which they are computed. This system is evaluated using a database containing files of four different sound-sources under a variety of recording conditions. Two neural networks are trained with the magnitude of the discrete Fourier transform of the LPC-cepstral matrices. The global percentage of verification was of 96.66%. The percentage of verification can be improved if the number of feature vectors (coefficients) is incremented in the neural network-training phase. Basically the idea here is to apply the techniques founded in speech recognition systems to an environmental sounds recognition system.

Published in:

Electrical and Electronics Engineering, 2005 2nd International Conference on

Date of Conference:

7-9 Sept. 2005