Sound event detection method based on hole convolution recurrent neural network

Abstract

The invention discloses a sound event detection method based on a cavity convolution recurrent neural network. The method comprises the following steps: extracting logarithm Mel spectrum characteristics of each sample, constructing a cavity convolution recurrent neural network, wherein the cavity convolution recurrent neural network comprises a convolution neural network, a bidirectional long-short-term memory neural network and a Sigmoid output layer, using logarithm Mel spectrum features extracted from a training sample as input to train the cavity convolution recurrent neural network, and identifying a sound event in the test sample by adopting the trained cavity convolution recurrent neural network to obtain a sound event detection result. According to the method, cavity convolution isintroduced into a convolutional neural network, and the convolutional neural network and a recurrent neural network are optimized and combined to obtain a hole convolution recurrent neural network. Compared with a traditional convolutional neural network, the void convolutional recurrent neural network has a larger receptive field under the condition that the sizes of the network parameter sets are the same, contextual information of audio samples can be more effectively utilized, and a better sound event detection result is obtained.

Description

technical field [0001] The invention relates to the technical field of audio signal processing and pattern recognition, in particular to a sound event detection method based on a hole convolutional cyclic neural network. Background technique [0002] The goal of Sound Event Detection (SED) is to accurately identify various target sound events in audio recordings. Acoustic event detection can be applied in many fields related to machine listening, such as traffic monitoring, smart meeting rooms, autonomous driving assistance, and multimedia analysis. Classifiers for sound event detection include deep and shallow models. Depth models mainly include convolutional cyclic neural networks, cyclic neural networks, and convolutional neural networks. Shallow models mainly include random regression forests, support vector machines, hidden Markov models, and Gaussian mixture models. [0003] The existing mainstream sound event detection method based on convolutional neural network h...

AI Technical Summary

Problems solved by technology

[0003]The existing mainstream sound event detection method based on convolutional neural network has the following shortcomings: In order to increase the receptive field and capture the longer context information of the input audio features, it is necessary to Increasing the number of convolutional layers of the network makes the scale of network parameters very large, which is easy to cause over-fitting problems (the generalization ability of the network decreases)

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