In this paper we present a robust noise estimation for speech enhancement algorithms. The robust noise estimation based on a modified minima controlled recursive averaging noise estimator was applied to different speech estimators. The investigated speech estimators were spectral substraction (SS), log spectral amplitude speech estimator (LSA) and optimally modified log spectral amplitude estimator (OM-LSA). The performance of the different algorithms were measured both by the signal-tonoise ratio (SNR) and recognition accuracy of an Automatic Speech Recognition (ASR)
Speech enhancement improves the quality of speech by removing certain amount of noise from noisy spe...
The objective of this paper is threefold: ( 1) to provide an extensive review of signal subspace spe...
Abstract — Development of preprocessing algorithms for speech enhancement is always of great interes...
Many compensation techniques, both in the model and feature domain, require an estimate of the noise...
Accurate noise power spectrum estimation in a noisy speech signal is a key challenge problem in spee...
[[abstract]]Accurately estimating noise magnitude can improve the performance of a speech enhancemen...
Log-Spectral Amplitude estimator (MMSE-LSA) noise power spectrum estimation algorithm does not adapt...
[[abstract]]Abstract The accuracy of noise estimation is important for the performance of a speech d...
We conduct a comparative study to investigate two noise es-timation approaches for robust speech rec...
[[abstract]]The accuracy of noise estimation is important for the performance of a speech denoising ...
We consider estimation of the noise spectral variance from speech signals contaminated by highly non...
In this paper, the improved noise tracking algorithm for speech enhancement is proposed. This method...
Signal Subspace (SS) based speech enhancement techniques obtain significant additive-noise reduction...
This paper addresses the problem of noise estimation for the Karhunen-Loeve transform (KLT) based sp...
Noise estimation is an important part for noisy speech enhancement due to its momentous effect on th...
Speech enhancement improves the quality of speech by removing certain amount of noise from noisy spe...
The objective of this paper is threefold: ( 1) to provide an extensive review of signal subspace spe...
Abstract — Development of preprocessing algorithms for speech enhancement is always of great interes...
Many compensation techniques, both in the model and feature domain, require an estimate of the noise...
Accurate noise power spectrum estimation in a noisy speech signal is a key challenge problem in spee...
[[abstract]]Accurately estimating noise magnitude can improve the performance of a speech enhancemen...
Log-Spectral Amplitude estimator (MMSE-LSA) noise power spectrum estimation algorithm does not adapt...
[[abstract]]Abstract The accuracy of noise estimation is important for the performance of a speech d...
We conduct a comparative study to investigate two noise es-timation approaches for robust speech rec...
[[abstract]]The accuracy of noise estimation is important for the performance of a speech denoising ...
We consider estimation of the noise spectral variance from speech signals contaminated by highly non...
In this paper, the improved noise tracking algorithm for speech enhancement is proposed. This method...
Signal Subspace (SS) based speech enhancement techniques obtain significant additive-noise reduction...
This paper addresses the problem of noise estimation for the Karhunen-Loeve transform (KLT) based sp...
Noise estimation is an important part for noisy speech enhancement due to its momentous effect on th...
Speech enhancement improves the quality of speech by removing certain amount of noise from noisy spe...
The objective of this paper is threefold: ( 1) to provide an extensive review of signal subspace spe...
Abstract — Development of preprocessing algorithms for speech enhancement is always of great interes...