An Improved Rocciho Algorithm for Music Mood Classification

The amount of information that is available on internet related to music is very huge. The information related to music can be mined using many features, and the on-line contribution of both musical experts and general listeners has provided music researchers with a rich resource of information. The mood of a song helps in recommending songs to online users. Also, there is a strong application-oriented interest in mood classification for music download services and audio players allow music collection browsing using mood as one search criteria. This paper proposes a novel mood classification technique using improved Roccihio algorithm. Since Rocchio algorithm uses only one prototype vector for representing a class, it offers a less prediction accuracy. We addressed this problem by considering the $k$-nearest vectors along with prototype vector. The proposed method is validated using real music data, collected from well known music portals, in comparison with other machine learning methods.

[1]  Andreas F. Ehmann,et al.  Lyric Text Mining in Music Mood Classification , 2009, ISMIR.

[2]  W. Thompson,et al.  A Cross-Cultural Investigation of the Perception of Emotion in Music: Psychophysical and Cultural Cues , 1999 .

[3]  P SomanK.,et al.  CENNLP at SemEval-2018 Task 1: Constrained Vector Space Model in Affects in Tweets , 2018, SemEval@NAACL-HLT.

[4]  Tuomas Eerola,et al.  Generalizability and Simplicity as Criteria in Feature Selection: Application to Mood Classification in Music , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[5]  Kiyoaki Shirai,et al.  Machine Learning Approaches for Mood Classification of Songs toward Music Search Engine , 2009, 2009 International Conference on Knowledge and Systems Engineering.

[6]  K. P. Soman,et al.  Semantic Analysis Using Pairwise Sentence Comparison with Word Embeddings , 2017 .

[7]  Francesco Archetti,et al.  UP-DRES User Profiling for a Dynamic REcommendation System , 2006, ICDM.

[8]  Jieping Xu,et al.  MUSIC MOOD CLASSIFICATION BASED ON VOTING , 2009 .

[9]  Shengxiao Guan,et al.  Text categorization based on improved Rocchio algorithm , 2012, 2012 International Conference on Systems and Informatics (ICSAI2012).

[10]  Yiming Yang,et al.  A re-examination of text categorization methods , 1999, SIGIR '99.

[11]  Peter Butala,et al.  Condition monitoring and fault diagnostics for hydropower plants , 2014, Comput. Ind..

[12]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[13]  Éric Gaussier,et al.  A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation , 2005, ECIR.

[14]  Stefanie Nowak,et al.  Content-based mood classification for photos and music: a generic multi-modal classification framework and evaluation approach , 2008, MIR '08.

[15]  Jens Grivolla,et al.  Multimodal Music Mood Classification Using Audio and Lyrics , 2008, 2008 Seventh International Conference on Machine Learning and Applications.

[16]  G. Widmer,et al.  EVALUATION OF FREQUENTLY USED AUDIO FEATURES FOR CLASSIFICATION OF MUSIC INTO PERCEPTUAL CATEGORIES , 2005 .

[17]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[18]  Pierre Baldi,et al.  Assessing the accuracy of prediction algorithms for classification: an overview , 2000, Bioinform..

[19]  Ichiro Fujinaga,et al.  Using jWebMiner 2.0 to Improve Music Classification Performance by Combining Different Types of Features Mined from the Web , 2010, ISMIR.

[20]  Tanujit Chakraborty,et al.  A novel hybridization of classification trees and artificial neural networks for selection of students in a business school , 2018 .

[21]  Chastine Fatichah,et al.  Music Emotion Classification based on Lyrics-Audio using Corpus based Emotion , 2018, International Journal of Electrical and Computer Engineering (IJECE).

[22]  Shouning Qu,et al.  Improvement of Text Feature Selection Method Based on TFIDF , 2008, 2008 International Seminar on Future Information Technology and Management Engineering.

[23]  G P Sajeev,et al.  A novel approach for book recommendation systems , 2016 .

[24]  J. Stephen Downie,et al.  A framework for evaluating multimodal music mood classification , 2017, J. Assoc. Inf. Sci. Technol..

[25]  G. P. Sajeev,et al.  Effective web personalization system based on time and semantic relatedness , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).