Real-time noise canceling based on spectral minimum detection and diffusive gain factors

Abstract

In this paper, we propose a very simple but highly effective psychoacoustically motivated real-time approach on the basis of spectral minimum detection and diffusive gain factors without a speech activity detector. The first processing step is the calculation of the short-time power spectrum of the noisy speech signal. Estimating the background noise, the system calculates diffusive gain values in real time being obtained in a two-layer structure: Each node of a layer is responsible for a single mode of the power spectrum. The first layer, called the ‘‘minimum detection layer,’’ holds the present noise level derived from the minimum of the input power spectrum which is detected within frames smaller than the FFT window. The minimum is transformed into a gain factor function using a signal-to-noise ratio control parameter. The diffusive gain factor interaction of neighboring modes is performed in the second layer, called the ‘‘diffusion layer,’’ in order to avoid ‘‘musical tones.’’ In the frequency domain, a filtering operation is performed by multiplying the noisy speech power spectrum by the diffusive gain factors to yield the filtered signal spectrum. This latter is transformed to the time domain by an inverse Fourier transform with original noisy phase.