A Compositional Automatic Music Transcription System for Computersynthesized Music

Abstract

Automatic music transcription can be widely used for content-based music retrieval, low rate coding and automatic musical accompaniment system and so on. Multiple fundamental frequency estimation, or Multiple-F0 estimation, is one of the most important problems in automatic music transcription, but it has not been well resolved up to now. This paper presents a machine learning methods using harmonic matching and iterative deletion for computer-synthesized music specifically to Multiple-F0 estimation and builds an efficient automatic music transcription system. Computersynthesized music is almost free from similar instruments of the differences between different individuals, so it is a good research object. It can be shown in this paper that the experimental results indicate that this method has very good recognition results.