Speech enhancement with kalman filter

Speech enhancement techniques aims to improve the quality or intelligibility of speech signals contaminated with background noise and can be implemented both in time and frequency domains. Spectral Subtraction, one of the most feasible methods in practice, is an effective way to enhance the noisy speech signals. However, a residual noise called musical noise occurs with the estimated speech signal and this is the major inconvenience of Spectral Subtraction.Wiener Filter is an alternative approach for speech enhancement in the manner of Spectral Subtraction filter. The drawback of the Wiener Filter is the fixed frequency response at all frequencies and the requirement to estimate the power spectral density of the clean signal and the noise prior to filtering.Kalman filtering is also one of the most effective methods in speech enhancement. In recent years, due to its magnificent accurate estimation characteristics especially in the research field of navigation and GPS, researchers tried to manipulate its advantages for useful purposes in signal processing.However, to improve the speech signals with the Kalman Filter, some parameters such as the AR coefficients of the clean signal and the noise covariance matrix must be known. Determining the AR coefficients of clean speech signal plays a crucial role for the success of the Kalman Filter while the only noisy observations are available. In such condition it is very difficult to estimate these parameters and today researches on this issue are ongoing.In this study, these parameters necessary to implement the Kalman Filter is determined using Spectral Subtraction. First of all, Spectral Subtraction, Wiener Filter and Kalman Filter is analyzed respectively. Then the AR coefficients of a speech signal is calculated using both Kalman Filter and the method of Linear Predictive Coding (LPC) that is frequently used in the literature.All three methods mentioned above for speech enhancement are carried out for speech signals corrupted with different types of noise. Finally, Kalman Filter combined with Spectral Subtraction proposed in this study is applied to those signals and all results are compared based on output SNR values as an objective measurement for the enhancement performance.Considering the obtained results, combined Kalman filter provided a better SNR improvement compared to the Wiener filter and Spectral Subtraction. Also combined Kalman filter suppressed the musical noise that occurred owing to Spectral Subtraction