Infant cry detection deep learning method

The invention discloses an infant cry detection deep learning method, and relates to the technical field of voice signal processing. The method comprises the following steps: a, acquiring a voice signal; b, framing the voice signal segment and performing cochlear voice feature extraction on each frame; and c, inputting the adjacent N frames of voice features into a pre-trained baby cry detection deep learning model for reasoning and judging whether cry exists or not and d, voting the N frames of classification results by applying a majority-first voting principle, and judging whether baby cryexists in the N frames or not. The cochlear speech features adopted by the method are speech feature parameters more conforming to auditory perception analysis of people, and the convolutional networkand the long-short-term memory recurrent neural network are adopted as acoustic inference models for infant cry detection, so that the method can adapt to a speech environment with a low signal-to-noise ratio, and has higher