Sound event detection and positioning method based on deep learning

Abstract

The invention relates to a sound event detection and positioning method based on deep learning. The method comprises the following steps of: step 1, segmenting a data set; step 2, preprocessing, namely performing feature extraction on the data set containing the sound signal to obtain a Log-Mel spectrogram and GCC-PHAT; step 3, constructing a deep learning model, namely constructing a network architecture combining a ResNet framework and RNN by referring to the ResNet framework, and compounding a pooling module, a regularization module and a normalization module between layers for optimizing feature extraction and improving nonlinearity; and step 4, two-step training, namely firstly training the SED task to obtain an optimal model and inputting the training result into DOA task training asa feature; and then training the DOA task to finally obtain an optimal training model. According to the method, the characteristics suitable for task training are extracted firstly, so that the reverberation resistance is improved, a new frame structure is provided to solve the problem that the precision is reduced due to network deepening, and finally the prediction precision is improved.

Description

technical field [0001] The invention relates to a sound event detection and positioning method based on deep learning, which is applied in technical fields such as robotics, natural science, environmental monitoring, and navigation. Background technique [0002] In recent years, with the development of digital signal processing technology and neural network technology, sound localization technology has made great progress. For example, Soumitro et al. proposed a single-source DOA (DirectionOf Arrival, direction of arrival estimation) estimation method based on CNN (convolutional neural network), which is to perform short-term Fourier transform on the signal received by the microphone, and then use the phase component as The input of the entire CNN network is passed through three layers of convolutional layers and two layers of fully connected layers, and the softmax activation function is used to obtain the hierarchical posterior probability of the output. Experiments show ...

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Problems solved by technology

[0005] Aiming at the deficiencies of the prior art, the present invention proposes a sound event detection and location method based on deep learning. This method solves the problems of poor anti-reverberation performance and network deepening resulting in poor anti-reverberation performance in sound event detection and location using the existing deep learning model. For the problem of descent, use two-step training, that is, first perform the SED part to detect the occurrence and offset of the sound event, and further associate the text label with the detected sound event; then perform the DOA part of the training to calculate the error of locating the sound source position

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