Enhancement of the coded speech using filtering ; Filtreleme kullanarak kodlanmış sesin iyileştirilmesi

Text in English ; Abstract: English and Turkish ; Includes bibliographical references (leaves 71-77) ; xi, 77 leaves ; The processing and storage of speech signals are widely implemented in modern communication systems. Decreasing the amount of information for modeling the reconstruction of speech signal enhances the transmission and storage capacity of the system. It is important to compress speech without losing its important properties during transmission or reconstruction independently from the speaker and speech signals itself. However, some losses inevitably occur in every compression process. Increasing the compression ratio results in increased losses. Speech enhancement algorithms may be used to enhance strongly compressed speech signals for better intelligibility and quality. The purpose of this study is to enhance speech with healing algorithms that compress speech signals while reducing background noise. The SYMPES [1][2][4] algorithm used in this study compresses data resulting in lesser loss than other known compression algorithms. As a result of the compression, noise occurs in the background. The type of the noise cannot be classified. Attempts have been made to reduce these background noises (distortions) by using di_erent methods of speech enhancement algorithms. More than ten speech enhancement algorithms have been investigated and implemented. Two algorithms with the best-enhanced sound output were determined and compared. One of them, Spectral Subtraction Algorithm, was applied via a geometric approach, which was investigated in 2008 by Yang Lu and Philipos C. Loizou [3].In this algorithm, a noise spectrum is subtracted from the noisy speech signal and then a clean signal spectrum is obtained. Moreover, in the absence of the signal, the noise spectrum can be updated and predicted. This approach expressed that the noise spectrum is not signi_cantly di_erent between update periods and is a noisy cum stationary or slowly changing process. Forward and inverse Fourier transforms are used in the ...