Noise reduction for mobile telephony applications is considered. A power spectral density error analysis is applied to the method of power subtraction. A minimum power spectral density error subtraction factor is derived, where the resulting factor depends on introduced quality factors. The outcome of the analysis is compared with experimental work as well as standardized noise reduction schemes.

Spectral subtraction is a widely employed method for the suppression of additive noise in speech signals. Several variants of the basic approach have been proposed over the years to address certain shortcomings, chiefly the quality of the remnant noise and its trade-off with speech distortion. In this paper, we present a unified view of the various forms of the spectral subtraction speech enhan...

The corruption of speech due to presence of additive background noise causes severe difficulties in various communication environments. This paper presents a novel noise reduction technique based upon a combination of cascaded spectral subtraction and Wiener filter methods in wavelet domain. The scheme’s performance is illustrated by experiments in a noisy car environment, in comparison with sp...

Robust speaker feature extraction under noise conditions is an important issue for application of a speaker recognition system. It is well known that LPC cepstrum, which expresses the spectral envelope, is e ective for speaker recognition. This implies that the spectral rough structure is e ective for speaker recognition. However, LPC cepstrum is a noise-sensitive feature. On the other hand, sp...

The traditional power spectral subtraction algorithm is computationally simple to implement but suffers from musical noise distortion. In addition, the subtractive rules are based on incorrect assumptions about the cross terms being zero. A new geometric approach to spectral subtraction is proposed in the present paper that addresses these shortcomings of the spectral subtraction algorithm. A m...

This work is mainly focused on showing experimental results using a combination of two methods for noise compensation which are shown to be complementary: classical spectral subtraction algorithm and histogram equalization. While spectral subtraction is focused on the reduction of the additive noise in the spectral domain, histogram equalization is applied in the cepstral domain to compensate t...

This work is mainly focused on showing experimental results using a combination of two methods for noise compensation which are shown to be complementary: classical spectral subtraction algorithm and histogram equalization. While spectral subtraction is focused on the reduction of the additive noise in the spectral domain, histogram equalization is applied in the cepstral domain to compensate t...

This paper presents a novel signal trajectory based noise compensation algorithm for robust speech recognition. Its performance is evaluated on the Aurora 2 database. The algorithm consists of two processing stages: 1) noise spectrum is estimated using trajectory autosegmentation and clustering, so that spectral subtraction can be performed to roughly estimate the clean speech trajectories; 2) ...

This paper describes a spatial spectral subtraction method by using the complementary beamforming microphone array to enhance noisy speech signals for speech recognition. The complementary beamforming is based on two types of beamformers designed to obtain complementary directivity patterns with respect to each other. In this paper, it is shown that the nonlinear subtraction processing with com...