A pattern recognition system for environmental sound classification based on MFCCs and neural networks

A pattern recognition system for environmental sound classification based on MFCCs and neural networks

The paper proposes a study of a background noise classifier based on a pattern recognition approach using a neural network. The signals submitted to the neural network are characterised by means of a set of 12 MFCC (Mel frequency cepstral coefficient) parameters typically present in the front end of...

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Bibliographic Details
Published in:2008 2nd International Conference on Signal Processing and Communication Systems pp. 1 - 4
Main Authors: Beritelli, F., Grasso, R.
Format: Conference Proceeding
Language:English
Published: IEEE 01-12-2008
Subjects:
Acoustic noise
Background noise
Hidden Markov models
Mel frequency cepstral coefficient
Neural networks
Noise robustness
Pattern recognition
Phase noise
Speech analysis
Speech processing
Online Access:Get full text
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Summary:The paper proposes a study of a background noise classifier based on a pattern recognition approach using a neural network. The signals submitted to the neural network are characterised by means of a set of 12 MFCC (Mel frequency cepstral coefficient) parameters typically present in the front end of a mobile terminal. The performance of the classifier, evaluated in terms of percent misclassification, indicate an accuracy ranging between 73% and 95% depending on the duration of the decision window.
DOI:10.1109/ICSPCS.2008.4813723