Approximate Nearest Neighbour-based Index Tree: A Case Study for Instrumental Music Search

Abstract Many people are interested in instrumental music. They may have one piece of song, but it is a challenge to seek the song because they do not have lyrics to describe for a text-based search engine. This study leverages the Approximate Nearest Neighbours to preprocess the instrumental songs and extract the characteristics of the track in the repository using the Mel frequency cepstral coefficients (MFCC) characteristic extraction. Our method digitizes the track, extracts the track characteristics, and builds the index tree with different lengths of each MFCC and dimension number of vectors. We collected songs played with various instruments for the experiments. Our result on 100 pieces of various songs in different lengths, with a sampling rate of 16000 and a length of each MFCC of 13, gives the best results, where accuracy on the Top 1 is 36 %, Top 5 is 4 %, and Top 10 is 44 %. We expect this work to provide useful tools to develop digital music e-commerce systems.