malpanivedant / kalman-filter-and-weiner-filter-comparision-for-speech-enhancement

Mean-square-error comparison between Kalman and Weiner Filter to determine better process for Speech Enhancement.

Kalman-Filter-and-Weiner-Filter-Comparision-for-Speech-Enhancement

Mean-square-error comparison between Kalman and Weiner Filter to determine better process for Speech Enhancement.

Speech enhancement has been a hot research area in recent years with the fast development of multimedia communications and other application. The presence of background noise in speech significantly reduces the intelligibility of speech. Noise reduction or speech enhancement algorithms are used to suppress such background noise and improve the perceptual quality and intelligibility of speech. Removing various types of noise is difficult due to the random nature of the noise and the inherent complexities of the speech. Noise reduction techniques usually have a trade-off between the amount of noise removal and speech distortions introduced due to processing of the speech signal. Several techniques have been proposed for this purpose in the area of speech Enhancement, like spectral subtraction approach, wiener filter, Kalman filter, weighted filter. The performance of these techniques depends on the quality and intelligibility of the processed speech signal. The improvement in the speech signal to noise ratio is the target of most techniques.

For detailed discription check:

https://github.com/malpanivedant/Kalman-Filter-and-Weiner-Filter-Comparision-for-Speech-Enhancement/blob/master/Comparision%20between%20Kalman%20Filter%20%26%20Weiner%20Filter.pdf