Sound classification system is one important application for pattern recognition. Recent years, sound classification system benefits a lot from the research in the field of pattern recognition such as automatic speech recognition, etc, and is becoming a reasonable selection as independent research aspect. However, the technique of sound classification has many questions to be solved, which focuses on how to get more robust feature from sound signal and how to improve the training process to get robust models based on the current technique of feature extraction. This paper will present in detail the author's research work on these problems in the courts of study as a master candidate. Firstly, we do some work to understand the speaker recognition technique based on speech signal, especially on text-independent speaker recognition. After the analysis of some factors in speaker recognition, we set up one text-independent speaker recognition system. We find some unreasonable question on distance measurement and the neglect of the mutual information between data of different sound classes. So based on these factors, we proposed a novel method, namely Covariance-tied Clustering Method, to mining the mutual information between data of different classes, and effectively take in much information of data distribution for distance measurement. This method can avoid data sparseness to some purpose. Secondly, based on the GMMs system frame of text-independent speaker rec6gnition, we developed the system of sound monitor for oil pipeline. This system gets better performance in the application. In the development of oil system, we analyzed the MFCC method of feature extraction for speech signal, and improve it for the oil sound. In the same time, in order to fit the realization in hardware, we achieved the integer program for the sound classification system, and modified the recognition process to save more time while the recognition rate is almost invariable. Thirdly, during the course of developing the sound monitor for oil pipeline, we collect the sounds of oil pipeline and constructed a corpus of actual sound and ensure the farther analysis of pipeline sounds.
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