Method for sound source direction estimation based on time frequency masking and deep neural network

Abstract

The invention discloses a method and device for sound source direction estimation based on time frequency masking and a deep neural network, electronic equipment and a storage medium, and belongs to the field of computer technologies. The method comprises the steps of acquiring a multichannel sound signal; carrying out framing, windowing and Fourier transform on each channel sound signal in the multichannel sound signal so as to form a short-time Fourier spectrum of the multichannel sound signal; carrying out an iterative operation on the short-time Fourier spectrum through a pre-trained neural network model, calculating ratio membranes corresponding to target signals in the multichannel sound signal, and fusing the multiple ratio membranes to form a single ratio membrane; and marking andweighting the multichannel sound signal according to the single ratio membrane to determine the orientation of the target sound source. The method and device for sound source direction estimation based on the time frequency masking and the deep neural network can have strong robustness in the environment with a low signal-to-noise ratio and strong reverberation, and improve the accuracy and stability of direction estimation for the target sound source.

Description

technical field [0001] The present disclosure relates to the field of computer application technology, and in particular to a sound source direction estimation method, device, electronic equipment, and storage medium based on time-frequency masking and deep neural networks. Background technique [0002] Sound source localization in noisy environments has many real-life applications, such as human-computer interaction, robotics, and beamforming. Traditionally, GCC-PHAT (Generalized Cross Correlation Phase Transform, generalized cross-correlation-phase transformation method), SRP-PHAT (Steered Response Power Phase Transform, phase transformation weighted controllable response power method) or MUSIC (Multiple Signal Classification, multiple Signal classification) and other sound source localization algorithms are the most common. However, these algorithms can only localize the loudest signal sources in the environment, which may not be the target speaker at all. For example, ...

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Problems solved by technology

[0004] In order to solve the technical problem of poor robustness of orientation estimation, the present disclosure provides a sound source direction estimation method, device, electronic equipment, and storage medium based on time-frequency masking and deep neural network

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