Evaluation of Audio FeatureExtraction Techniques to ClassifySynthesizer Sounds

Abstract

After many years focused on speech signal processing, the research in audio processing started to investigate the field of music processing. Music Information Retrieval is a very new topic steadily growing since a few years as music is more and more part of our daily life, particularly thanks to the new technologies like mp3 players and smartphones. Moreover, with the development of electronic music and the huge improvements in computational power, new instruments have appeared such as virtual instruments, bringing with them new needs concerning the availability of sounds. One main necessity which came with these novel technologies is to have a user friendly system to make it easy for the users to have access to the whole range of sounds the device can offer.  In this thesis, the purpose is to implement a smart automatic classification of synthesizer sounds based on audio descriptors without any human influence. Hence the study first focus on what is a musical sound and what are the main characteristics of synthesizer sounds that need to be extracted using wisely chosen audio descriptors extraction. Then the interest moves to a classifier system based on the Self-Organizing Map model using unsupervised learning to match with the main purpose to avoid any human bias and use only objective parameters for the sounds classification. Finally the evaluation of the system is done, showing that it gives good results both in terms of accuracy and time efficiency