Conventional single-channel speech enhancement methods implement the analysis-modification-synthesis (AMS) framework in the acoustic frequency domain. Recently, it has been shown that the extension of this framework to the modulation domain may result in better noise suppression. However, this conclusion has been reached by relying on a minimum statistics approach for the required noise power spectral density (PSD) estimation.Various noise estimation algorithms have been proposed over the years in the speech and audio processing literature. Among these, the widely used minimum statistics approach is known to introduce a time frame lag in the estimated noise spectrum. This can lead to highly inaccurate PSD estimates when the noise behaviour rapidly changes with time, i.e., non-stationary noise. Speech enhancement methods which employ these inaccurate noise PSD estimates tend to perform poorly in the noise suppression task, and in worst cases, may end up deteriorating the noisy speech signal even further. Noise PSD estimation algorithms using a priori information about the noise statistics have been shown to track non-stationary noise better than the conventional algorithms which rely on the minimum statistics approach.In this thesis, we perform noise suppression in the modulation domain with the noise and speech PSD derived from an estimation scheme which employs the a priori information of various speech and noise types.Specifically, codebooks of gain normalized linear prediction coefficients obtained from training on various speech and noise files are used as the a priori information while performing the estimation of the desired PSD.The PSD estimates derived from this codebook approach are used to obtain a minimum mean square error (MMSE) estimate of the clean speech modulation magnitude spectrum, which is then combined with the phase spectrum of the noisy speech to recover the enhanced speech signal. The enhanced speech signal is subjected to various objective experiments for evaluation. Results of these evaluations indicate improvement in noise suppression with the proposed codebook-based modulation domain approach over competing approaches, particularly in cases of non-stationary noise.
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