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

Abstract

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 based on psychoacoustic and music-theoretic representations of the audio for this application. Finally, a simplified version of our system is re-incorporated into MoodSwings as a simulated partner for single-players, providing a potential platform for furthering perceptual studies and modeling of musical mood.

Affective gesturing with music mood recognition

“…One hour of music was analyzed by human experts to mark A-V values, and then passed into the system [7]. This average error of the system was less than 15% of the total space when taking arousal and valence together, and less than 12% treating them separately.…”

“…2). We have also obtained annotations of the arousal and valence (A-V) values in audio from human users, as this ground-truth data is invaluable in training emotion recognition systems [7]. Finally, our group has developed several methods for automatic music emotion recognition.…”

Ranking Tag Pairs for Music Recommendation Using Acoustic Similarity

“…For each music segment in each frame, the MER system was trained to detect the emotion type in each segment. In contrast, an MER system is able to assume that music emotion is continuously changing according to time [10]. This approach expresses the emotional content of a music clip as a function of time-varying musical features [5].…”

“…This study was performed with ordinary people from several cultural backgrounds based on adjectives that were selected by subjects. As shown in Figure 1(a), Hevner summarized the words used by the subjects to an impressive 66 adjectives, and these words were further extended [10]. Because of its intuitive representation, there are a series of studies using these emotional adjectives to represent music emotions [3].…”

Music-aided affective interaction between human and service robot

“…General approaches have concentrated on acoustic features representing the musical mood and criteria for the classification of moods [19][20][21]. A recent study focused on a context-based approach that uses contextual information such as websites, tags, and lyrics [22].…”