Music Information Retrieval and Audio Recommendation System Based on MPEG-7 Audio

In this thesis, the author proposes a music informational retrieval system and an audio recommendation system using MPEG-7 Audio. In the beginning, Melody and Audio Signature Description Scheme which are relevant to music are introduced. For music informational retrieval system, the goal is to retrieve the MPEG-7 Melody Description Scheme from the MIDI input and use them to find the music clips most similar to the queries from the data base. During the melodic contour extraction, the system applies the highest pitch method to overcome the chord problem existed in melody; local alignment, an algorithm in dynamic programming, is utilized to calculated the similarity. The Xerces C++ XML parser is adopted in the system in order to parse the MPEG-7 files. Finally, a rating result is constructed for system performance evaluation. On other hand, the audio recommendation system introduces the content-based filtering approach. Depending on the music data from the user’s preference, the system extracts the corresponding MPEG-7 audio signature and employs LBG Vector Quantization for classification. A new music input may be classified, and be given a score to decide whether the song is recommended. In the part of music information retrieval system, the recognition rate could be arrived at 65% when the first similar candidate at contour length 10 is found in 1242 MIDI files. Then the recognition rate will rise with the increase of contour lengths. In the audio recommendation system’s part, we let two people to give the ratings for 571 wav and MP3 songs according to their preference and some songs are all rated by the recommendation system. After comparing the rating by human and the system, we discovered that the average correct recommendation ratio could reach 75%.