الفهرس | Only 14 pages are availabe for public view |
Abstract This thesis deals with the problem of blind separation of audio signals from noisy mixtures. It proposes the application of a blind separation algorithm on the Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), or Discrete Wavelet Transform (DWT) of the mixed signals, instead of performing the separation on the mixtures in the time domain. Noise reduction techniques are tested as pre- and post- processing techniques to enhance the performance of the blind signal separation process. Both the DCT and the DST have an energy compaction property, which concentrates most of the signal energy in a few coefficients in the transform domain, leaving most of the transform-domain coefficients close to zero. As a result, the separation is performed on a few coefficients in the transform domain. Another advantage of signal separation in transform domains is that the effect of noise on the signals in the transform domains is smaller than that in the time domain due to the averaging effect of the transform equations, especially, when the separation algorithm is preceded by a wavelet denoising step. The simulation results confirm the superiority of transform-domain separation to time domain separation and the importance of the wavelet denoising step. As a practical case, we propose the application of the different proposed techniques of blind signal separation to improve the recognition rate of a speaker identification system. A comparison study is held between performances of the speaker identification systems with and without the application of the blind signal separation algorithms in time and transform domains. The simulation results show a significant improvement in the performance of the automatic speaker identification system with blind signal separation. |