Noise Estimation of Non-Stationary Voiced Speech Using Chirp Model
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 12
Abstract
Speech Enhancement algorithmic models removes or reduces the noise and improve one or more perceptual aspects of noisy speech most notably quality and intelligibility. The speech signal is considered as Non-stationary components, for instance time varying amplitudes and frequencies, which may change quickly over short time intervals by harmonic chirp model. Minimum Variance (MV) filter and Amplitude EStimation (AES) filter are derived and used in removal for noise, enhancement of speech, and parameter estimation to measure the quality of clean speech. In other words, the filtering approaches proposed herein provide full parameterizations of periodic signals through the use of filters based on chirp model. Noise covariance matrix based on MV and AES filters is generated to measure the noise power and reduce the noise present in speech signals. MUSIC algorithm is used to calculate the pseudo-spectral noise power in signals applied. Performance of MV and AES filters and the quality of speech enhanced are measured in terms of Speech Intelligibility Index (SII) and Mutual Information (MI). Simulation on noise corrupted speech signal result in a denoised clean speech with the SII value of 0.8 and MI of 0.17, and the values are compared with the input speech values. Results shows that the SII value and MI value of clean speech are increased by 0.3 and 0.5 from the input speech signal values.
Authors and Affiliations
Omana. B. , Bini Palas. P.
Keywords
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