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Speech enhancement under nonstationary environments is a challenging problem. This paper addresses the problem of speech presence probability (SPP) estimation. According to the fact that speech is approximately sparse in time-frequency domain, we integrate time and frequency minimum tracking results to estimate the noise power spectral density and the a posteriori signal-to-noise ratio. A sparseness...
We present a novel noise power estimation method based on smoothed spectral minima tracking and subtractive blocking matrix for dual-channel speech enhancement. By combining spectral characteristics of the noisy mixture signals with spatial null beam-forming, noise over- and under-estimation problem can be substantially mitigated. The proposed noise estimation tested on the real-life nonstationary...
This paper study the blind estimation of the diffuse background noise for the hands-free speech interface. Some recent papers showed that it is possible to use blind signal separation (BSS) to estimate the diffuse background noise by suppressing the speech component after all the components were separated. In particular, the scale indeterminacy of BSS is avoided by using the projection back method...
In this paper a new iterative method of speech enhancement using Power Spectral Density (PSD) codebooks of clean speech and several types of noise, is proposed. The proposed algorithm estimates the PSDs of speech and noise of unknown nature and, evaluates the input Signal-to-Noise Ratio (SNR) by solving an over-determined set of equations. No Voice Activity Detection (VAD) or other means of noise...
For speech enhancement before low bit rate speech coding in adverse environments, an improved noise estimation approach is proposed to keep speech enhancement performance with lower complexity. First, the noisy speech is transformed into Bark domain and the minima values are tracked. Then, these values are applied to control the smoothing factor of recursive averaging in noise estimator. The proposed...
Many cochlear implant (CI) users are able to understand speech in quiet listening conditions, however, CI users' speech recognition deteriorates rapidly as the level of background noise increases. To make CI more applicable in reallife environments, noise reduction is needed in CI processor. Recently, we presented a psychoacoustically-motivated adaptive beta-order generalized spectral subtraction...
This paper presents a low-complexity algorithm for tracking the noise spectral variance of speech contaminated by non-stationary noise sources. The proposed algorithm is based upon a recursive refinement process in which each step of the algorithm expectation of the instantaneous noise power is calculated based on information from the incoming signal and the current estimated distribution parameters,...
In this paper, we use spectral subtraction to remove non-stationary industrial noise from speech signal. Motor generated industrial noises often contain a wide-band part and some limited number of dominant sinusoidal signals that they carry most of the noise energy, especially, at low frequencies. The latter components highly drop the speech signal quality. To fulfil the requirements of spectral subtraction,...
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