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A sound source localization method based on convolutional neural network cnn

A convolutional neural network and sound source localization technology, applied in the information field, can solve problems such as reverberation and noise, and achieve good positioning effect and convenient and simple data collection

Active Publication Date: 2021-03-23
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

This method effectively solves the problems of noise and reverberation in traditional sound source localization

Method used

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  • A sound source localization method based on convolutional neural network cnn
  • A sound source localization method based on convolutional neural network cnn
  • A sound source localization method based on convolutional neural network cnn

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Embodiment Construction

[0053] The invention will be described in further detail below in conjunction with the accompanying drawings.

[0054] Such as figure 1 As shown, a sound source localization method based on convolutional neural network CNN, including the following:

[0055] Step 1: Use the Roomsim toolkit to simulate the indoor environment with reverberation and noise, according to the signals received by the two microphones x 1 (t) and x 2 (t), calculate the cross-correlation function of the signal received by the microphone Then perform frame-by-frame interception and feature extraction to obtain a training set to prepare for the next step of model training.

[0056] The specific steps of feature extraction are as follows:

[0057] Step 1.1: The sound source is located at the training position r s , s=1, 2, ..., k, the microphone array records the signal s(t) sent by the sound source at this position;

[0058] Step 1.2: Calculate the reverberation signal x received by the microphone a...

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Abstract

The invention provides a sound source localization algorithm based on a convolutional neural network (CNN). In this method, by introducing the characteristics of the identification cross-correlation function, using the simulation environment of Roomsim and the signals received by two microphones, the cross-correlation function in the environment with reverberation and noise is obtained, and the training set and test set are obtained by frame-by-frame interception. , the feature is trained to obtain a convolutional neural network, that is, the CNN model. During the training process, the ReLU function is used as the activation function, and the test set is used to estimate the sound source localization under the model, and finally the Bayesian decision is used to construct the decision The formula determines the category of the test sample, so that the conditional probability p(r s |Y) is the position where the real position of the sound source is estimated. The implementation of this algorithm effectively solves the problems of noise and reverberation in traditional sound source localization.

Description

technical field [0001] The invention relates to a research on a sound source localization method based on a neural network (CNN), and belongs to the field of information technology. Background technique [0002] For the sound source localization algorithm, how to improve the anti-noise and anti-reverberation capabilities has been the research focus for a long time. In the actual environment, when the signal-to-noise ratio is small and the reverberation is serious, the improvement measures based on traditional algorithms Hard to have a noticeable effect. In addition, when the microphone cannot receive the direct sound of the sound source, it is also difficult to locate. The present invention proposes to use a convolutional neural network (CNN) to identify a phase-transformed weighted generalized cross-correlation function (GCC-PHAT) for sound source localization. Experiments show that the convolutional neural network (CNN) has better localization performance in low signal-t...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S5/18G06N3/04
CPCG01S5/18G06N3/045
Inventor 万新旺王吉廖鹏程陈中倩
Owner NANJING UNIV OF POSTS & TELECOMM
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