Algorithm Research On Blind Separation Of Speech Signals

Blind source separation is a process of recovery source signals only according to observed mixed signals. It finds broad application owing not to require the priori knowledge of signals and the blind separation of speech signals becomes research hotspot because of its practicability. The research of blind speech signals separation is important for computer hearing and speech recognition, and high-quality speech communication, aid-hearing and telephony remote conference system can be profit from it. Therefore, the research of blind speech signals separation has very important value of theoretic and application.This paper is researched the Blind speech signals separation. The uppermost contributions are as follows:1. This paper presents an algorithm of blind speech signals separation based on the minimization of the mutual information and MLPs, RBF neural network. The algorithm is using the infomax algorithm, and combining the information backpropagation theory of neural network, optimizing objective function, making the yield components that are as independent as possible during the course of the separating the mixing signals. This paper compared the optimum performance of separated speech signals between two neural network, the experiment indicate that the algorithm can be successful separate the mixing speech signals, and the RBF is better than MLPs. 2. The best window structure of time-frequence for blind speech signals separation has been put forward in this paper. Due to the Length and shape of multiple Windows faction have important influence for the performance of separated speech signals; this paper experimented and compared it. The conclusion reveals that where the window length is 256, the separated signals can be attain the best effect for the quasi-stationary nature and inherent correlation of speech over the temporal short term.3. On the temporal short term of speech signals, we detailed analyses the relationship for frame size and mutual information. The statistically independent increase for speech as the frame size decreases, where the frame sizes less than 100 ms, the mutual information value dramatic increase exhibited by the speech signals. The results indicate that although algorithm of blind source separation is suitable for application with speech in batch techniques possessing substantial data, it is inevitably less reliable for realistic audio environments that require a real-time approach to separation.