Content-based music retrieval using linear scaling and branch-and-bound tree search

Abstract

paper presents the use of linear scaling and tree search in a content-based music retrieval system that can take a user's acoustic input (8-second clip of singing or humming) via a microphone and then retrieve the intended song from over 3000 candidate songs in the database. The system, known as Super MBox, demonstrates the feasibility of real-time content-based music retrieval with a high recognition rate. Super MBox first takes the user's acoustic input from a microphone and converts it into a pitch vector. Then a fast comparison engine using linear scaling and tree search is employed to compute the similarity scores. We have tested Super MBox and found the top-20 recognition rate is about 73% with about 1000 clips of test inputs from people with mediocre singing skills.