Auditory perception property-based signal subspace microphone array voice enhancement method

Abstract

The invention discloses an auditory perception property-based signal subspace microphone array voice enhancement method, which fully combines auditory masking effect of human ears based on the traditional microphone array signal subspace voice enhancement method. The algorithm core of the signal subspace is to reasonably estimate a linear filter, and the key point of the algorithm comprises the following steps: accurately estimating the dimensionality and noise power spectrum of the signal subspace, and reasonably estimating a Lagrangian multiplier. Therefore, the invention provides a feasible solution, which comprises the following steps: performing time domain alignment, short-time Fourier transform and power spectrum characteristic value decomposition on signals acquired by a microphone array; determining the dimensionality of a noise subspace by hypothesis testing; estimating a noise power spectrum by a conditional probability method in the noise subspace; estimating an auditory masking threshold value based on the signal subspace; and estimating the linear filter by combining the Lagrangian multiplier according to the auditory perception property.

Description

technical field [0001] The invention relates to a signal subspace method of a microphone array, the masking effect of human ear hearing and the design of a post-filter. Background technique [0002] Microphone array speech enhancement methods have been extensively studied in recent years. Among them, the signal subspace algorithm has an excellent ability to eliminate additive broadband noise. The signal subspace algorithm decomposes the noisy signal space into signal subspace (including target speech signal and noise) and noise subspace (only contains noise), and estimates the target speech signal in the signal subspace. The core of the signal subspace algorithm is to estimate the linear filter reasonably, and one of the main points is to accurately estimate the signal subspace dimension and noise power spectrum. The research on the signal subspace speech enhancement method has proved that the method has good speech enhancement performance. Although the signal subspace al...

AI Technical Summary

Problems solved by technology

Although the signal subspace algorithm has superior performance, it is still quite difficult to completely eliminate noise

Usually, after the signal subspace algorithm denoises, there will still be some residual noise in the enhanced speech, which reduces the perceived quality of the speech

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