Sound source target recognition system based on wearable devices

Abstract

The invention belongs to the technical field of sound signal processing, and in particular relates to a sound source target recognition system based on a wearable device. In the technical solution of the present invention, firstly, two sets of training dense convolution encoding-decoding networks train the abstract high-level features of the encoding network for the input data and manually extracted features, and the use of dense convolution can enable us to train a deeper encoding network and Enhanced use of repeated features. Secondly, the fusion layer is designed to fuse the features extracted by the two encoding networks, and finally a convolutional network is used to classify the sound source signal. Finally, the technical solution of the present invention solves the problems in the prior art that have a negative impact on the recognition accuracy, and that the convolutional network still has defects such as gradient disappearance or explosion and feature reuse rate when the number of layers is deepened.

Description

technical field [0001] The invention belongs to the technical field of sound signal processing, and in particular relates to a sound source target recognition system based on a wearable device. Background technique [0002] Sound source target recognition is one of the two key technologies of intelligent robot system - visual target recognition and sound source target recognition. Especially when the intelligent robot is in the scene of occlusion, smoke, camouflage, interference, etc., and the optical and electromagnetic detection equipment cannot be used, the acoustic sensor can still maintain the robot's perception of the environment, realize the sound source target recognition, and assist the robot control and decision-making. [0003] The difficulty of sound source target recognition is to accurately identify targets from complex noise environments. Traditional sound source target recognition is based on feature engineering methods, that is, features that have been arti...

AI Technical Summary

Problems solved by technology

Theoretically, there is still room for further improvement in the convolutional neural network. Since the convolutional neural network is based on local connections and weight sharing, it is difficult for the convolutional filter to extract the overall high-frequency (edge ​​and detail) information, which will negatively affect the recognition accuracy. influences

In addition, when the number of layers of the convolutional network is deepened, there are defects such as gradient disappearance or explosion, and feature repetition rate.

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