In the present day, the music becomes more popular due to the following three reasons: (1) the evolution of the MP3 compression technology, (2) the growth of the public platform, and (3) the development of the MP3 portable discs. Most people follow the music to hum or follow the rhythm to tap sometimes. The meanings of a music style may be various if it is explained or felt by different people. Therefore we cannot obtain a very explicit answer if the notation of the music cannot be exactly made. We need some techniques and methods to analyze the music, and obtain some of its embedded information. Tempo and beats are very important elements in the perceptual music. Therefore, tempo estimation and beat tracking are fundamental techniques in automatic audio processing, which are crucial to multimedia applications. In this thesis, we first develop an artificial neural network to classify the music excerpts into the evaluation preference. And then, with the preference classification, we can obtain accurate estimation for tempo and beats, by either Ellis's method or Dixon's method. We test our method with a mixed data set which contains ten music genres extracted from the "ballroom dancer" database. Our experimental results show that the accuracy of our method is higher than that of only one individual Ellis's method or Dixon's method.