Music Based Mood Detection

Abstract

Music mood describes the inherent emotional expression of a music clip. It is helpful in music understanding, music retrieval, and some other music-related applications. In this paper, a hierarchical framework is presented to automate the task of mood detection from acoustic music data, by following some music psychological theories in western cultures. The hierarchical framework has the advantage of emphasizing the most suitable features in different detection tasks. Three feature sets, including intensity, timbre, and rhythm are extracted to represent the characteristics of a music clip. The intensity feature set is represented by the energy in each sub-band, the timbre feature set is composed of the spectral shape features and spectral contrast features, and the rhythm feature set indicates three aspects that are closely related with an individual’s mood response, including rhythm strength, rhythm regularity, and tempo. Furthermore, since mood is usually changeable in an entire piece of classical music, the approach to mood detection is extended to mood tracking for a music piece, by dividing the music into several independent segments, each of which contains a homogeneous emotional expression. Preliminary evaluations indicate that the proposed algorithms produce satisfactory results. On our testing database composed of 800 representative music clips, the average accuracy of mood detection achieves up to 86.3%. We can also on average recall 84.1% of the mood boundaries from nine testing music pieces. A method is proposed for detecting the emotions of song lyrics based on an affective lexicon. The lexicon is composed of words translated from ANEW and words selected by other means. For each lyric sentence, emotion units, each based on an emotion word in the lexicon, are found out, and the influences of modifiers and tenses on emotion units are taken into consideration. The emotion of a sentence is calculated from its emotion units. Tofigure out the prominent emotions of a lyric, a fuzzy clustering method is used to group the lyric’s sentences according to their emotions. The emotion of a cluster is worked out from that of its sentences considering the individual weight of each sentence. Clusters are weighted according to the weights and confidences of their sentences and singing speeds of sentences are considered as the adjustment of the weights of clusters. Finally, the emotion of the cluster with the highest weight is selected from the prominent emotions as the main emotion of the lyric. The performance of our approach is evaluated through an experimentof emotion classification of 400 song lyrics. Therefore, the main idea is to merge the two ideas altogether.