Rapid development of modern information technology, multimedia technology and network technology has resulted in large and ever-increasing stores of multimedia data. Therefore, content-based audio information retrieval (CBAIR) technology has been attracting more and more attention to the efforts to make full use of existing audio information. Audio data can be static or dynamic, and the audio retrieval can be at expression level or semantic level. Different audio form and different retrieval level requires different retrieval methods. Although much work has been done in relevant researches, there still exist many unsolved difficulties in CBAIR area. The major difficulties include the following: the performance of most present retrieval methods deteriorates dramatically under noise; it is very difficult to index audio data which is highly dimensional and of time sequence little work has been done on dynamic audio retrieval; research in the semantic-level retrieval for audio music progresses hard and slowly due to the serious difficulties in extracting semantic information from audio music). Generally speaking, CBAIR technology is still at experimental stage and lacks applicable technology and system.Centering around the problems existing in CBAIR, this dissertation studies the following problems:1. For the problem of expression-level retrieval of static audio, fuzzy histogram audio retrieval method based on principal loudness component is developed. In the design of histogram model, statistical distribution of loudness is used to optimize the histogram model. At the same time, fuzzy histogram is used to improve the robustness of histogram model to noise and small change in loudness value. Active search method is used in the histogram retrieval. Experimental results show that the method has better robustness to noise.2. For the problem of expression-level indexing of static audio, a novel indexing method based on fuzzy histogram of principal loudness component is presented. When audio data is expressed by fuzzy histogram of principal loudness component, the similarity between their histograms can correctly reflect