Instrument classification in polyphonic music based on timbre analysis

While most previous work on musical instrument recognition is focused on the classification of single notes in monophonic music, a scheme is proposed in this paper for the distinction of instruments in continuous music pieces which may contain one or more kinds of instruments. Highlights of the system include music segmentation into notes, harmonic partial estimation in polyphonic sound, note feature calculation and normalization, note classification using a set of neural networks, and music piece categorization with fuzzy logic principles. Example outputs of the system are "the music piece is 100% guitar (with 90% likelihood)" and "the music piece is 60% violin and 40% piano, thus a violin/piano duet". The system has been tested with twelve kinds of musical instruments, and very promising experimental results have been obtained. An accuracy of about 80% is achieved, and the number can be raised to 90% if misindexings within the same instrument family are tolerated (e.g. cello, viola and violin). A demonstration system for musical instrument classification and music timbre retrieval is also presented.