A method for Music Classification Based On Perceived Mood Detection for Indian Bollywood Music
A lot of research has been done in the past decade in the field of audio content analysis for extracting various information from audio signal. One such significant information is the "perceived mood" or the "emotions" related to a music or audio clip. This information is extremely useful in applications like creating or adapting the play-list based on the mood of the listener. This information could also be helpful in better classification of the music database. In this paper we have presented a method to classify music not just based on the meta-data of the audio clip but also include the "mood" factor to help improve the music classification. We propose an automated and efficient way of classifying music samples based on the mood detection from the audio data. We in particular try to classify the music based on mood for Indian bollywood music. The proposed method tries to address the following problem statement: Genre information (usually part of the audio meta-data) alone does not help in better music classification. For example the acoustic version of the song "nothing else matters by Metallica" can be classified as melody music and thereby a person in relaxing or chill out mood might want to listen to this track. But more often than not this track is associated with metal / heavy rock genre and if a listener classified his play-list based on the genre information alone for his current mood, the user shall miss out on listening to this track. Currently methods exist to detect mood in western or similar kind of music. Our paper tries to solve the issue for Indian bollywood music from an Indian cultural context
[1] Carolyn J. Murrock,"Music and Mood," in Psychology of Moods 2005
[2] D. Huron, "Perceptual and cognitive applications in music information
retrieval," in Proc. Int. Symp. Music Information Retrieval (ISMIR),2000.
[3] Ellis, D.P.W.; Poliner, G.E.; , "Identifying ÔÇÿCover Songs- with Chroma
Features and Dynamic Programming Beat Tracking," Acoustics, Speech
and Signal Processing, 2007. ICASSP 2007. IEEE International Conference
on , vol.4, no., pp.IV-1429-IV-1432, 15-20 April 2007
[4] E. Scheirer, "Tempo and beat analysis of acoustic musical signals," J.
Acoust. Soc. Amer., vol. 103, no. 1, pp. 588601, 1998.
[5] Peeters, G.; , "Spectral and Temporal Periodicity Representations of
Rhythm for the Automatic Classification of Music Audio Signal," Audio,
Speech, and Language Processing, IEEE Transactions on, vol.19, no.5,
pp.1242-1252, July 2011
[6] Tsunoo, E.; Tzanetakis, G.; Ono, N.; Sagayama, S.; , "Beyond Timbral
Statistics: Improving Music Classification Using Percussive Patterns and
Bass Lines," Audio, Speech, and Language Processing, IEEE Transactions
on, vol.19, no.4, pp.1003-1014, May 2011
[7] Mancini, M.; Bresin, R.; Pelachaud, C.; , "A Virtual Head Driven by
Music Expressivity," Audio, Speech, and Language Processing, IEEE
Transactions on, vol.15, no.6, pp.1833-1841, Aug. 2007
[8] Lie Lu; Liu, D.; Hong-Jiang Zhang; , "Automatic mood detection and
tracking of music audio signals," Audio, Speech, and Language Processing,
IEEE Transactions on, vol.14, no.1, pp. 5- 18, Jan. 2006
[9] Proc. ISMIR: Int. Symp. Music Information Retrieval, [Online].
http://www.ismir.net/.
[10] Thayer, Robert E. (1998). "The Biopsychology of Mood and Arousal".
New York, NY: Oxford University Press
[11] Picard, Rosalind."Affective Computing" MIT Technical Report #321,
1995
[12] P. N. Juslin; P. Laukka, "Communication of emotions in vocal expression
and music performance: Different channels, same code?," Psychol.
Bull.,vol. 129, no. 5, pp. 770814, 2003.
[13] Pachet, F.; Roy, P.; , "Improving Multilabel Analysis of Music Titles: A
Large-Scale Validation of the Correction Approach," Audio, Speech, and
Language Processing, IEEE Transactions on, vol.17, no.2, pp.335-343,
Feb. 2009
[14] Hu, Xiao.; J. Stephen Downie,"Exploring mood metadata: Relationships
with genre, artist and usage metadata,"International Conference on Music
Information Retrieval (ISMIR 2007),Vienna, September 23-27, 2007
[15] L.-L. Balkwill; W. F. Thompson; R. Matsunag, "Recognition of emotion
in Japanese, Western, and Hindustani music by Japanese listeners",
Japanese Psychological Research, Volume 46, No. 4, 337349, 2004
[16] P. N. Juslin, "Cue utilization in communication of emotion in music
performance: relating performance to perception," J. Exper. Psychol.:
Human Percept. Perf.,vol. 16, no. 6, pp. 17971813, 2000.
[17] Patrik N. Juslin, John A. Sloboda, "Handbook of music and emotion:
theory, research, applications"
[18] Olivier Lartillot; Petri Toiviainen,"A Matlab Toolbox For Musical Feature
Extraction From Audio.," in Proc. of the 10th Int. Conference on
Digital Audio Effects (DAFx-07),Bordeaux, France, September 10-15,
2007
[19] Siemer M. (2005). "Moods as multiple-object directed and as objectless
affective states: An examination of the dispositional theory of moods."
Cognition & Emotion: January 2008. Vol 22, Iss. 1; p. 815-845.
[20] J. A. Russell, "A circumplex model of affect," J. Personality Social
Psychology,vol. 39, pp. 11611178, 1980.
[21] G. Tzanetakis and P. Cook, "Multifeature audio segmentation for browsing
and annotation," in Proceedings of the 1999 IEEE Workshop on
Applications of Signal Processing to Audio and Acoustics,1999.
[22] William Moylan, "The art of recording: understanding and crafting the
mix"
[23] Rossitza Setchi, "Knowledge-Based And Intelligent Information And
Engineering Systems:" 14th International Conference,KES 2010, Cardiff,
UK, September 8-10, 2010
[24] Eerola, T., Lartillot, O., and Toiviainen, P. "Prediction of multidimensional
emotional ratings in music from audio using multivariate regression
models." In Proceedings of 10th International Conference on Music
Information RetrievalISMIR 2009, pages 621-626.
[25] Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter
Reutemann, Ian H. Witten (2009); "The WEKA Data Mining Software:
An Update"; SIGKDD Explorations, Volume 11, Issue 1.
[26] Ross Quinlan (1993). C4.5: "Programs for Machine Learning, Morgan
Kaufmann Publishers", San Mateo, CA.
[27] Meng, A.; Ahrendt, P.; Larsen, J.; Hansen, L.K.; , "Temporal Feature
Integration for Music Genre Classification," Audio, Speech, and Language
Processing, IEEE Transactions on, vol.15, no.5, pp.1654-1664, July 2007
[28] Hanjalic, A.; , "Extracting moods from pictures and sounds: towards
truly personalized TV," Signal Processing Magazine, IEEE, vol.23, no.2,
pp.90-100, March 2006
[29] Changsheng Xu; Maddage, N.C.; Xi Shao; , "Automatic music classification
and summarization," Speech and Audio Processing, IEEE Transactions
on, vol.13, no.3, pp. 441- 450, May 2005
[1] Carolyn J. Murrock,"Music and Mood," in Psychology of Moods 2005
[2] D. Huron, "Perceptual and cognitive applications in music information
retrieval," in Proc. Int. Symp. Music Information Retrieval (ISMIR),2000.
[3] Ellis, D.P.W.; Poliner, G.E.; , "Identifying ÔÇÿCover Songs- with Chroma
Features and Dynamic Programming Beat Tracking," Acoustics, Speech
and Signal Processing, 2007. ICASSP 2007. IEEE International Conference
on , vol.4, no., pp.IV-1429-IV-1432, 15-20 April 2007
[4] E. Scheirer, "Tempo and beat analysis of acoustic musical signals," J.
Acoust. Soc. Amer., vol. 103, no. 1, pp. 588601, 1998.
[5] Peeters, G.; , "Spectral and Temporal Periodicity Representations of
Rhythm for the Automatic Classification of Music Audio Signal," Audio,
Speech, and Language Processing, IEEE Transactions on, vol.19, no.5,
pp.1242-1252, July 2011
[6] Tsunoo, E.; Tzanetakis, G.; Ono, N.; Sagayama, S.; , "Beyond Timbral
Statistics: Improving Music Classification Using Percussive Patterns and
Bass Lines," Audio, Speech, and Language Processing, IEEE Transactions
on, vol.19, no.4, pp.1003-1014, May 2011
[7] Mancini, M.; Bresin, R.; Pelachaud, C.; , "A Virtual Head Driven by
Music Expressivity," Audio, Speech, and Language Processing, IEEE
Transactions on, vol.15, no.6, pp.1833-1841, Aug. 2007
[8] Lie Lu; Liu, D.; Hong-Jiang Zhang; , "Automatic mood detection and
tracking of music audio signals," Audio, Speech, and Language Processing,
IEEE Transactions on, vol.14, no.1, pp. 5- 18, Jan. 2006
[9] Proc. ISMIR: Int. Symp. Music Information Retrieval, [Online].
http://www.ismir.net/.
[10] Thayer, Robert E. (1998). "The Biopsychology of Mood and Arousal".
New York, NY: Oxford University Press
[11] Picard, Rosalind."Affective Computing" MIT Technical Report #321,
1995
[12] P. N. Juslin; P. Laukka, "Communication of emotions in vocal expression
and music performance: Different channels, same code?," Psychol.
Bull.,vol. 129, no. 5, pp. 770814, 2003.
[13] Pachet, F.; Roy, P.; , "Improving Multilabel Analysis of Music Titles: A
Large-Scale Validation of the Correction Approach," Audio, Speech, and
Language Processing, IEEE Transactions on, vol.17, no.2, pp.335-343,
Feb. 2009
[14] Hu, Xiao.; J. Stephen Downie,"Exploring mood metadata: Relationships
with genre, artist and usage metadata,"International Conference on Music
Information Retrieval (ISMIR 2007),Vienna, September 23-27, 2007
[15] L.-L. Balkwill; W. F. Thompson; R. Matsunag, "Recognition of emotion
in Japanese, Western, and Hindustani music by Japanese listeners",
Japanese Psychological Research, Volume 46, No. 4, 337349, 2004
[16] P. N. Juslin, "Cue utilization in communication of emotion in music
performance: relating performance to perception," J. Exper. Psychol.:
Human Percept. Perf.,vol. 16, no. 6, pp. 17971813, 2000.
[17] Patrik N. Juslin, John A. Sloboda, "Handbook of music and emotion:
theory, research, applications"
[18] Olivier Lartillot; Petri Toiviainen,"A Matlab Toolbox For Musical Feature
Extraction From Audio.," in Proc. of the 10th Int. Conference on
Digital Audio Effects (DAFx-07),Bordeaux, France, September 10-15,
2007
[19] Siemer M. (2005). "Moods as multiple-object directed and as objectless
affective states: An examination of the dispositional theory of moods."
Cognition & Emotion: January 2008. Vol 22, Iss. 1; p. 815-845.
[20] J. A. Russell, "A circumplex model of affect," J. Personality Social
Psychology,vol. 39, pp. 11611178, 1980.
[21] G. Tzanetakis and P. Cook, "Multifeature audio segmentation for browsing
and annotation," in Proceedings of the 1999 IEEE Workshop on
Applications of Signal Processing to Audio and Acoustics,1999.
[22] William Moylan, "The art of recording: understanding and crafting the
mix"
[23] Rossitza Setchi, "Knowledge-Based And Intelligent Information And
Engineering Systems:" 14th International Conference,KES 2010, Cardiff,
UK, September 8-10, 2010
[24] Eerola, T., Lartillot, O., and Toiviainen, P. "Prediction of multidimensional
emotional ratings in music from audio using multivariate regression
models." In Proceedings of 10th International Conference on Music
Information RetrievalISMIR 2009, pages 621-626.
[25] Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter
Reutemann, Ian H. Witten (2009); "The WEKA Data Mining Software:
An Update"; SIGKDD Explorations, Volume 11, Issue 1.
[26] Ross Quinlan (1993). C4.5: "Programs for Machine Learning, Morgan
Kaufmann Publishers", San Mateo, CA.
[27] Meng, A.; Ahrendt, P.; Larsen, J.; Hansen, L.K.; , "Temporal Feature
Integration for Music Genre Classification," Audio, Speech, and Language
Processing, IEEE Transactions on, vol.15, no.5, pp.1654-1664, July 2007
[28] Hanjalic, A.; , "Extracting moods from pictures and sounds: towards
truly personalized TV," Signal Processing Magazine, IEEE, vol.23, no.2,
pp.90-100, March 2006
[29] Changsheng Xu; Maddage, N.C.; Xi Shao; , "Automatic music classification
and summarization," Speech and Audio Processing, IEEE Transactions
on, vol.13, no.3, pp. 441- 450, May 2005
@article{"International Journal of Information, Control and Computer Sciences:64268", author = "Vallabha Hampiholi", title = "A method for Music Classification Based On Perceived Mood Detection for Indian Bollywood Music", abstract = "A lot of research has been done in the past decade in the field of audio content analysis for extracting various information from audio signal. One such significant information is the "perceived mood" or the "emotions" related to a music or audio clip. This information is extremely useful in applications like creating or adapting the play-list based on the mood of the listener. This information could also be helpful in better classification of the music database. In this paper we have presented a method to classify music not just based on the meta-data of the audio clip but also include the "mood" factor to help improve the music classification. We propose an automated and efficient way of classifying music samples based on the mood detection from the audio data. We in particular try to classify the music based on mood for Indian bollywood music. The proposed method tries to address the following problem statement: Genre information (usually part of the audio meta-data) alone does not help in better music classification. For example the acoustic version of the song "nothing else matters by Metallica" can be classified as melody music and thereby a person in relaxing or chill out mood might want to listen to this track. But more often than not this track is associated with metal / heavy rock genre and if a listener classified his play-list based on the genre information alone for his current mood, the user shall miss out on listening to this track. Currently methods exist to detect mood in western or similar kind of music. Our paper tries to solve the issue for Indian bollywood music from an Indian cultural context
", keywords = "Mood, music classification, music genre, rhythm, music
analysis.", volume = "6", number = "12", pages = "1777-8", }
