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In communication system environment speech signal is corrupted due to presence of additive acoustic noise, so with this distortion the effective communication is degraded in terms of the quality and intelligibility of speech. Now present research is going how effectively acoustic noise can be eliminated without affecting the original speech quality, this tends to be our challenging in this current research thesis work. Here this work proposes multi-tiered detection method that is based on time-frequency analysis (i.e. filter banks concept) of the noisy speech signals, by using standard speech enhancement method based on the proven spectral subtraction, for single channel speech data and for a wide range of noise types at various noise levels. There were various variants have been introduced to standard spectral subtraction proposed by S.F.Boll. In this thesis we designed and implemented a novel approach of Spectral Subtraction based on Minimum Statistics [MinSSS]. This means that the power spectrum of the non-stationary noise signal is estimated by finding the minimum values of a smoothed power spectrum of the noisy speech signal and thus circumvents the speech activity detection problem. This approach is also capable of dealing with non-stationary noise signals. In order to analyze the system in time frequency domain, we have implemented two different filter bank approaches such as Weighted OverLap Added (WOLA) and Fast Fourier Transform Modulated (FFTMod). The proposed systems were implemented and evaluated offline using simulation tool Matlab and then validated their performances based on the objective quality measures such as Signal to Noise Ratio Improvement (SNRI) and Perceptual Evaluation Speech Quality (PESQ) measure. The systems were tested with a pure speech combination of male and female sampled at 8 kHz, these signals were corrupted with various kinds of noises at different noise power levels. The MinSSS algorithm implemented using FFTMod filter bank approach outperforms when compared the WOLA filter bank approach. |
