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ISSN Druckformat: 0040-2508
ISSN Online: 1943-6009
Indexed in
Environmental Sounds Recognition
ABSTRAKT
This paper describes an environmental sounds recognition system using LPC-Cepstral coefficients as feature vectors and an artificial neural network backpropagation as recognition method. LPC-Cepstral data are totally dependents of the sound-source from which are computed. This system is evaluated using a database containing files from four different sound-sources under a variety of recording conditions. The training patterns used in the network-training ad testing processes, are extracted from the Discrete Fourier transform magnitude of the LPC-Cepstral matrices. The global percentages of classification obtained in the network-testing process are 98.2% and 96.8%. Basically the idea here is to apply the techniques found in speech recognition systems to an environmental sounds recognition system.
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Ramadan Rabie A., Yadav Kusum, Nonlinear acoustic noise cancellation based automatic speech recognition system (NANC-ASR) with convolutional neural networks, International Journal of Speech Technology, 25, 3, 2022. Crossref