Architecture for Automated Tagging and Clustering of Song Files According to Mood
Music is one of the basic human needs for recreation and
entertainment. As song files are digitalized now a days, and
digital libraries are expanding continuously, which makes it
difficult to recall a song. Thus need of a new classification
system other than genre is very obvious and mood based
classification system serves the purpose very well.
In this paper we will present a well-defined architecture to
classify songs into different mood-based categories, using audio
content analysis, affective value of song lyrics to map a song
onto a psychological-based emotion space and information from
online sources. In audio content analysis we will use music
features such as intensity, timbre and rhythm including their subfeatures
to map music in a 2-Dimensional emotional space. In
lyric based classification 1-Dimensional emotional space is used.
Both the results are merged onto a 2-Dimensional emotional
space, which will classify song into a particular mood category.
Finally clusters of mood based song files are formed and
arranged according to data acquired from various Internet
sources.
Keywords: Music information retrieval, mood detection
from music, song classification, mood models, music
features, lyric processing
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