Fast music retrieval using polyphonic binary feature vectors

We propose a method for retrieving similar music from a polyphonic-music audio database using a polyphonic audio signal as a query. In this task, we must consider similarities among polyphonic signals of the music, and achieve quick retrieval. Therefore, we first introduce the polyphonic binary feature vector to represent the presence of multiple notes. This feature is suitable for the search based on the similarities among polyphonic audio signals. Then, we propose a new search method, which is quicker than the exhaustive use of DP matching. The search is accelerated using a "similarity matrix" to limit the search space. Experiments using a test database containing 216 music pieces show that the search accuracy of the proposed feature is 89%, which is approximately 26% higher than that of the conventional spectrum feature. It is also shown that the new search method retrieves similar music without significant accuracy degradation as well as the exhaustive search does and the computational complexity of the new search method is about 1/4 that of exhaustive search.