Method of denoising a noisy signal including speech and noise components
A method of analyzing time coherence in the noisy signal including the steps of: a) determining a reference signal from the noisy signal by applying treatment ( 10, 18 ) to the noisy signal that is suitable for attenuating speech components more strongly than the noise component, in particular by an adaptive recursive predictive algorithm of the LMS type; b) determining ( 24 ) a probability of speech being present/absent on the basis of the respective energy levels in the spectral domain of the noisy signal and of the reference signal; and c) deriving ( 26 ) a denoised estimate of the speech signal from the noise signal as a function of the probability of the speech being present/absent as determined in this way.
1. In a data processing apparatus, a method of denoising an original noisy signal, said original noisy signal including a speech component and a noise component, the noise component comprising a transient noise component and a pseudo-steady noise component, the method comprising analyzing time coherence of the sampled noisy signal comprising the steps of:
a) determining a reference signal by processing the original noisy signal by attenuating the speech components more strongly than the noise component, said processing comprising:
a1) applying an adaptive linear prediction algorithm operating on a linear combination of a plurality of samples of the noisy signal, said samples of said noisy signals temporally taken prior to said original noisy signal, to produce a predictive signal; and
a2) determining said reference signal by taking the difference, with compensation for phase offset, between the original noisy signal and the predictive signal delivered by the linear prediction algorithm;
b) determining probability of speech being absent on the basis of the respective energy levels in the spectral domain of the original noisy signal and of the reference signal; and
c) using said probability of the absence of speech to estimate a noise spectrum and deriving from the original noisy signal a denoised estimate of the speech signal;
wherein the noisy signal is received by a single microphone.
2. The method of claim 1 , in which said reference signal is determined by applying in step a2) a relationship of the type:
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where X(k,l) and Y(k,l) are the short-term Fourier transforms of each spectrum segment k of each frame l respectively of the original noisy signal and of the signal delivered by the linear prediction algorithm.
3. The method of claim 1 , in which the linear prediction algorithm is an algorithm of the least mean square (LMS) type.
4. The method of claim 1 , in which the linear prediction algorithm is a recursive adaptive algorithm.
5. The method of claim 1 , in which step b) comprises an algorithm for estimating the energy of the pseudo-steady noise component in the reference signal and in the noisy signal.
6. The method of claim 5 , in which the algorithm for estimating the energy of the pseudo-steady noise component is an algorithm of the minima controlled recursive averaging (MCRA) type.
7. The method of claim 1 , in which step c) further comprises applying a variable gain algorithm that is a function of the probability of speech being present/absent.
8. The method of claim 7 , in which the variable gain algorithm is an algorithm of the optimally-modified log-spectral amplitude (OM-LSA) gain type.
9. The method of claim 1 , wherein said data processing apparatus comprises a hands free apparatus for mobile telephones.