An overview of audio information retrieval
Multimedia Systems - Special issue on audio and multimedia
MARSYAS: a framework for audio analysis
Organised Sound
MARSYAS: a framework for audio analysis
Organised Sound
Natural language processing of lyrics
Proceedings of the 13th annual ACM international conference on Multimedia
Combination of audio and lyrics features for genre classification in digital audio collections
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Finding `Lucy in Disguise': The Misheard Lyric Matching Problem
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
Computational creativity tools for songwriters
CALC '10 Proceedings of the NAACL HLT 2010 Second Workshop on Computational Approaches to Linguistic Creativity
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Music genre classification using explicit semantic analysis
MIRUM '11 Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Unsupervised tagging of spanish lyrics dataset using clustering
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Multimedia content can be described in versatile ways as its essence is not limited to one view. For music data these multiple views could be a song's audio features as well as its lyrics. Both of these modalities have their advantages as text may be easier to search in and could cover more of the 'content semantics' of a song, while omitting other types of semantic categorisation. (Psycho) acoustic feature sets, on the other hand, provide the means to identify tracks that 'sound similar' while less supporting other kinds of semantic categorisation. Those discerning characteristics of different feature sets meet users' differing information needs. We will explain the nature of text and audio feature sets which describe the same audio tracks. Moreover, we will propose the use of textual data on top of low level audio features for music genre classification. Further, we will show the impact of different combinations of audio features and textual features based on content words.