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In this paper, we reveal new findings about the generated musical noise in minimum mean-square error short-time spectral amplitude (MMSE STSA) processing. Recently we have proposed a objective metric of musical noise based on kurtosis change ratio on spectral subtraction (SS). Also we found an interesting relationship among the degree of generated musical noise, the shapes of signal-s probability...
In this letter, we propose a novel acoustic echo suppression (AES) technique based on soft decision in a frequency domain. The proposed approach provides an efficient and unified framework for such procedures as AES gain computation, AES gain modification using soft decision, and estimation of relevant parameters based on the same statistical model assumption of the near-end and far-end signal instead...
Recently we have developed a non-linear feature-domain noise reduction algorithm based on the minimum mean square error (MMSE) criterion on Mel-frequency cepstra (MFCC) for environment-robust speech recognition. Our novel algorithm operates on the power spectral magnitude of the filter-bank's outputs and outperforms the log-MMSE spectral amplitude noise suppressor proposed by Ephraim and Malah in...
This paper proposes a two stage hybrid speech enhancement system with nonuniform subbands. Frequency bins after Fourier transform are nonuniformly grouped to reduce the computations in calculating the spectral gain. First stage includes a soft decision gain modification and applied to the Ephraim-Malah gain function based on minimum mean square error estimation (MMSE) and a psychoacoustic masking...
This paper proposes a new robust adaptive beamformer applicable to microphone arrays. The proposed beamformer is a Generalized Sidelobe Canceller (GSC) with a single-channel noise reduction stage. The single-channel stage can be either Optimally Modified Log-Spectral Amplitude (OMLSA) estimator or Adaptive Minimum Mean-Square (AMMSE) spectral amplitude estimator. These hybrid structures, named GSC-OMLSA/...
This letter considers the estimation of speech signals contaminated by additive noise in the discrete Fourier transform (DFT) domain. Existing complex-DFT estimators assume independency of the real and imaginary parts of the speech DFT coefficients, although this is not in line with measurements. In this letter, we derive some general results on these estimators, under more realistic assumptions....
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