Feature selection for content-based, time-varying musical emotion regression

In developing automated systems to recognize the emotional content of music, we are faced with a problem spanning two disparate domains: the space of human emotions and the acoustic signal of music. To address this problem, we must develop models for both data collected from humans describing their perceptions of musical mood and quantitative features derived from the audio signal. In previous work, we have presented a collaborative game, MoodSwings, which records dynamic (per-second) mood ratings from multiple players within the two-dimensional Arousal-Valence representation of emotion. Using this data, we present a system linking models of acoustic features and human data to provide estimates of the emotional content of music according to the arousal-valence space. Furthermore, in keeping with the dynamic nature of musical mood we demonstrate the potential of this approach to track the emotional changes in a song over time. We investigate the utility of a range of acoustic features ...

• Type: Conference
• Year: 2010
• Where: MIR
• Authors: Erik M. Schmidt, Douglas Turnbull, Youngmoo E. Kim
• Keywords: Acoustic Features, Emotional Content, MIR 2010, Multimedia, Musical Mood