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Deep learning-based binaural sound source positioning method in digital hearing aid

A technology of deep learning and sound source localization, which is applied in hearing aids, positioning, devices used to obtain desired pointing characteristics, etc., can solve the problems of not satisfying the real-time performance of hearing aids and the high complexity of sound source localization technology, and achieve low complexity , good real-time performance and high positioning accuracy

Active Publication Date: 2018-06-05
BEIJING UNIV OF TECH
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AI Technical Summary

Problems solved by technology

For most digital hearing aids, the sound source localization technology is highly complex and causes time delay, which cannot meet the real-time requirements of hearing aids

Method used

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  • Deep learning-based binaural sound source positioning method in digital hearing aid
  • Deep learning-based binaural sound source positioning method in digital hearing aid
  • Deep learning-based binaural sound source positioning method in digital hearing aid

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Embodiment Construction

[0020] Step 1. Using the human auditory perception theory, combining the auditory characteristics of the human ear and the working mechanism of the cochlea, pass the binaural speech signal into the gammatone filter and divide it into N channels, and extract the sensitive information of the human ear;

[0021] Due to the frequency division characteristics and auditory masking characteristics of the cochlea, the gammatone filter bank is used to decompose the speech signal into multiple channels. The gammatone filter is a cochlear basement membrane model based on the auditory model, which can better simulate the sharpness of the basement membrane. The filter characteristics of the filter are in line with the auditory perception characteristics of the human ear, and the realization of the filter is simple. Therefore, the gammatone filter bank is selected to decompose the signal of the noisy speech, so that it can simulate the auditory characteristics of the human ear. The time-doma...

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Abstract

The invention discloses a deep learning-based binaural sound source positioning method in a digital hearing aid. The method comprises the following steps: firstly, decomposing a binaural sound sourcesignal into a plurality of channels through a gammatone filter, extracting a high-energy channel through a weighting coefficient, then extracting a first type of features by using a head-related-transform function (HRTF), namely an Interaural Time Difference (ITD) and an Interaural Intensity Difference (IID) which are used as inputs of deep learning, and dividing a horizontal plane into four quadrants to narrow a positioning range; secondly, extracting a second type of features of head-related transform, namely an Interaural Level Difference (ILD) and an Interaural Phase Difference (IPD); andfinally, in order to realize more precise positioning, taking the first and second types of four features as inputs of next deep learning, thereby obtaining an azimuth angle of sound source positioning. Precise positioning of 72 azimuth angles is realized on the horizontal plane from 0 degree to 360 degrees at a step length of 5 degrees.

Description

technical field [0001] The invention belongs to the technical field of speech signal processing, and relates to a binaural sound source localization method based on deep learning in a digital hearing aid. Background technique [0002] Deafness has become a worldwide problem. For the deaf, choosing suitable digital hearing aids is the best way to help them improve their hearing. The basic working principle of digital hearing aids is as follows: figure 2 As shown, the external sound signal enters the microphone to convert sound energy into electrical energy, and then converts it into a digital signal through an analog / digital converter, and then uses a multi-channel loudness compensation algorithm, adaptive noise reduction algorithm, and echo cancellation algorithm in the DSP processor , frequency shifting algorithm and sound source localization and other technologies for processing, the processed digital electrical signal needs to be converted into an analog electrical sign...

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Application Information

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IPC IPC(8): G10L19/26G10L21/0264G10L25/06G10L25/30H04R25/00G01S5/20
CPCG01S5/20G10L19/26G10L21/0264G10L25/06G10L25/30H04R25/40
Inventor 李如玮潘冬梅李涛刘亚楠张永亚
Owner BEIJING UNIV OF TECH
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