Environmental Sounds Recognition System Using the Speech Recognition System Techniques
Omar A Uribe, Héctor Manuel Pérez Meana, Mariko Nakano-Miyatake · 2005
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.