基于Mel频谱值和深度学习网络的鸟声识别算法*

为了增强网络对鸟鸣声信号的特征学习能力并提高识别精度，提出一种基于深度残差收缩网络和扩张卷积的鸟声识别方法。首先，提取鸟鸣声信号的对数梅尔特征及其一阶和二阶差分系数组成logMel特征集作为网络模型的输入；其次，通过深度残差收缩网络自动学习噪声阈值，减少噪声干扰；然后，引入扩张卷积增大卷积核感受野并利用注意力机制使网络更关注关键帧特征；最后，通过双向长短时记忆网络从学到的局部特征中学习长期依赖关系。以百鸟数据birdsdata鸟声库中的19种中国常见鸟类作为实验对象，识别正确率可以达到96.58%，并对比模型在不同信噪比数据下的识别结果，结果表明该模型在噪声环境下的识别效果优于现有模型。

Bird voice recognition algorithm based on Mel spectrum value and deep learning network

In order to enhance the feature learning ability of the network to the birdsong signal and improve the recognition accuracy, a birdsong recognition method based on depth residual shrinkage network and expanded convolution is proposed. Firstly, the logarithmic Mel feature and its first-order and second-order difference coefficients of birdsong signal are extracted to form a logmel feature set as the input of the network model; Secondly, the noise threshold is automatically learned through the depth residual shrinkage network to reduce the noise interference; Then, the expanded convolution is introduced to increase the receptive field of convolution kernel, and the attention mechanism is used to make the network pay more attention to the characteristics of key frames; Finally, the long-term dependence is learned from the learned local features through the two-way long-term and short-term memory network. Taking 19 kinds of common Chinese birds in birdsdata as the experimental object, the recognition accuracy can reach 96.58%. Compared with the recognition results of the model under different signal-to-noise ratio data, the results show that the recognition effect of the model in noise environment is better than that of the existing model.