Self-adaptive acoustic feedback suppression method

Abstract

The invention discloses a self-adaptive acoustic feedback suppression method. On the basis of an LMS algorithm, by introducing a momentum factor, namely a prior error, and simultaneously combining with an NLMS algorithm, an update step of a self-adaptive algorithm is optimized and a convergence speed is accelerated, thereby adjusting a central frequency of a wave trap more quickly and reducing thedistortion of a finally obtained output signal. According to the self-adaptive acoustic feedback suppression method, through a self-adaptive wave trapping method, a change of a howling frequency canbe tracked and the central frequency of the wave trap is automatically adjusted. When sampling data are limited, relatively accurate frequency estimation precision can be obtained, and thus the distortion degree of the signal after wave trapping can be reduced. A recursive process is used by the self-adaptive algorithm and only time domain processing is involved, so an FFT operation does not needto be performed, and the calculation complexity is greatly reduced. By introducing the prior error and a changeable convergence factor to perform step adjustment, the convergence speed of the self-adaptive algorithm is further accelerated; and while the algorithm has relatively good acoustic feedback suppression effect, the timeliness on signal processing can be met.

Description

technical field [0001] The invention relates to an acoustic feedback algorithm in speech signal processing, and more particularly, to an adaptive acoustic feedback suppression method based on frequency tracking. Background technique [0002] Speech signal processing has been plagued by acoustic feedback problems since sound reinforcement systems became widely used. The common feature of the sound reinforcement system is to amplify the sound signal to obtain a larger output volume. In the sound reinforcement system, the sound is emitted by the loudspeaker. When the output signal of the loudspeaker is coupled into the microphone, a closed-loop feedback system is formed. When the system gain is too large, harsh howling sounds will be generated due to the acoustic feedback phenomenon. Acoustic feedback severely limits the improvement of system gain and will directly lead to signal distortion. What's more, acoustic feedback can burn out speakers and amplifiers. Therefore, effe...

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Problems solved by technology

However, the problem with the random phase method is that when the phase condition of one feedback path is destroyed, the phase condition of the other feedback path may be satisfied, resulting in a new howling frequency point, so the performance is not as good as other methods

Before notching, the howling frequency point must be detected first, which requires the power spectrum information of the signal to be obtained through short-time Fourier transform, which increases the processing delay of the system on the one hand; The setting of detection and notch bandwidth has a great dependence on

Especially for DSP systems, if there are too few sampling points per frame, the accuracy of howling frequency point detection may not be achieved

When the input signal is processed by the notch filter, if the value detected by the howling frequency point deviates greatly from the real value, and the setting of the notch bandwidth is unreasonable, not only will there be no notch effect, but other frequency components of the audio will be suppressed instead. causing greater distortion

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