Computational Auditory Scene Analysis: Principles, Algorithms, and Applications
Computational Auditory Scene Analysis: Principles, Algorithms, and Applications
Speech enhancement based on a priori signal to noise estimation
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
Subjective comparison and evaluation of speech enhancement algorithms
Speech Communication
On the optimality of ideal binary time-frequency masks
Speech Communication
Improved Signal-to-Noise Ratio Estimation for Speech Enhancement
IEEE Transactions on Audio, Speech, and Language Processing
Evaluation of Objective Quality Measures for Speech Enhancement
IEEE Transactions on Audio, Speech, and Language Processing
A coherence-based noise reduction algorithm for binaural hearing aids
Speech Communication
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Most noise reduction algorithms rely on obtaining reliable estimates of the SNR of each frequency bin. For that reason, much work has been done in analyzing the behavior and performance of SNR estimation algorithms in the context of improving speech quality and reducing speech distortions (e.g., musical noise). Comparatively little work has been reported, however, regarding the analysis and investigation of the effect of errors in SNR estimation on speech intelligibility. It is not known, for instance, whether it is the errors in SNR overestimation, errors in SNR underestimation, or both that are harmful to speech intelligibility. Errors in SNR estimation produce concomitant errors in the computation of the gain (suppression) function, and the impact of gain estimation errors on speech intelligibility is unclear. The present study assesses the effect of SNR estimation errors on gain function estimation via sensitivity analysis. Intelligibility listening studies were conducted to validate the sensitivity analysis. Results indicated that speech intelligibility is severely compromised when SNR and gain over-estimation errors are introduced in spectral components with negative SNR. A theoretical upper bound on the gain function is derived that can be used to constrain the values of the gain function so as to ensure that SNR overestimation errors are minimized. Speech enhancement algorithms that can limit the values of the gain function to fall within this upper bound can improve speech intelligibility.