An Efficient Tree-based Quantization for Content Based Music Retrieval System

In this paper, we have proposed and implemented a new music retrieval system based on content of music wave files. We have investigated different quantization methods by constructing them into the music data histograms as the feature vectors for the music files. There are three important aspects that will affect implementation of the system: audio feature extraction, quantization and distance computation. The proposed new system can allow the users to search the waveform data based on the query music samples. Without converting the audio wave file into a MIDI format, histograms are generated from the query data by vector quantization directly. By using the vector quantization, we can achieve the high accuracy in audio retrieval rate. In the proposed CBMR system, the use of 128 clusters in Kmeans-clustering quantization algorithm can achieve 87% retrieval accuracy and 90% high retrieval accuracy rate with the tree-based quantization. Key-Words: content based audio retrieval, K-means clustering, Tree-based quantization, MFCC