贝叶斯优化卷积神经网络公共场所异常声识别*

针对公共场所异常声的感知和识别问题,提出一种基于贝叶斯优化卷积神经网络的识别方法。提取声信号的Gammatone倒谱系数、倍频程功率谱、短时能量和谱质心,组合成声信号的特征图。构建卷积神经网络作为分类器,利用递增的卷积核设置和池化操作处理不同尺度的特征。基于贝叶斯优化算法优化卷积神经网络的模型参数,对包括火苗噼啪声、婴儿啼哭声、烟花燃放声、玻璃破碎声和警报声的5种公共场所异常声进行识别。该方法的识别结果与基于不同的特征提取和分类器方案得到的识别结果进行比较,结果表明该方法的识别效果优于其他特征提取和分类器方案的识别效果。最后分析了该方法在不同信噪比噪声干扰下的识别结果,验证了该方法的有效性。

Recognition of abnormal sound in public places based on Bayesian optimal convolutional neural network

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.