Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int