Recognition of abnormal sound in public places based on Bayesian optimal convolutional neural network
ZENG Yu;HU Wencheng;Beijing Municipal Institute of Labour Protection;
Aiming at the problem of abnormal sound perception and recognition in public places, a recognition method based on Bayesian optimal convolution neural network is proposed. The Gammatone cepstrum coefficients, octave power spectrum, short-term energy and spectral centroid of sound signal are extracted and combined to form the characteristic map of sound signal. Using convolution neural network as classifier, different convolution kernel settings and pooling operations are adopted to deal with different scales of features.Based on Bayesian optimization algorithm, the model parameters of convolution neural network are optimized.Five kinds of abnormal sounds in public places, including crackling of fire, crying of infants, fireworks, broken glass and alarms, are identified. Finally, the recognition results of different feature extraction and classifier schemes are compared, and the advantages of this method are illustrated. The recognition results of this method under noise jamming are analyzed, and the validity of this method is verified.