Acoustic Features for Music Emotion Recognition and System Building
Kanawat Sorussa, Anant Choksuriwong, Montri Karnjanadecha · 2017
We are faced with a massive growth of musical data in the form of digital files. Accurate metadata labeling of music archives is necessary in order to make digital music searchable and to be efficiently organized, not only by file name or song title but in deeper detail, such as genre, artist, and types of instrument. Music related emotional terms are frequently used as search keywords, especially for non-vocal music such as film soundtracks. Music emotion recognition is a challenging issue because there are no standard categories, and how music evokes emotion is poorly understood, and related to many factors. Manually labeling music emotion by an expert is infeasible when dealing with huge amounts of music and when opinion on the same musical piece can vary from person to person. A system that is able to reveal the hidden relationships between music-related factors and emotional perception is required. This paper discuses background knowledge on psychology, musicology, and acoustic feature extraction by MATLAB toolboxes. It also includes construction guidelines for a music emotion classification system.