Unveiling Music Structure Via PLSA Similarity Fusion

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2007

Standard

Unveiling Music Structure Via PLSA Similarity Fusion. / Arenas-García, Jerónimo; Meng, Anders; Petersen, Kaare Brandt; Lehn-Schiøler, Tue; Hansen, Lars Kai; Larsen, Jan.

2007 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING. Formerly the IEEE Workshop on Neural Networks for Signal Processing, August 27-29, 2007, Thessaloniki, Greece. IEEE, 2007. p. 419-424.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2007

Harvard

Arenas-García, J, Meng, A, Petersen, KB, Lehn-Schiøler, T, Hansen, LK & Larsen, J 2007, 'Unveiling Music Structure Via PLSA Similarity Fusion'. in 2007 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING. Formerly the IEEE Workshop on Neural Networks for Signal Processing, August 27-29, 2007, Thessaloniki, Greece. IEEE, pp. 419-424., 10.1109/MLSP.2007.4414343

APA

Arenas-García, J., Meng, A., Petersen, K. B., Lehn-Schiøler, T., Hansen, L. K., & Larsen, J. (2007). Unveiling Music Structure Via PLSA Similarity Fusion. In 2007 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING. Formerly the IEEE Workshop on Neural Networks for Signal Processing, August 27-29, 2007, Thessaloniki, Greece. (pp. 419-424). IEEE. 10.1109/MLSP.2007.4414343

CBE

Arenas-García J, Meng A, Petersen KB, Lehn-Schiøler T, Hansen LK, Larsen J. 2007. Unveiling Music Structure Via PLSA Similarity Fusion. In 2007 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING. Formerly the IEEE Workshop on Neural Networks for Signal Processing, August 27-29, 2007, Thessaloniki, Greece. IEEE. pp. 419-424. Available from: 10.1109/MLSP.2007.4414343

MLA

Arenas-García, Jerónimo et al. "Unveiling Music Structure Via PLSA Similarity Fusion". 2007 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING. Formerly the IEEE Workshop on Neural Networks for Signal Processing, August 27-29, 2007, Thessaloniki, Greece. IEEE. 2007. 419-424. Available: 10.1109/MLSP.2007.4414343

Vancouver

Arenas-García J, Meng A, Petersen KB, Lehn-Schiøler T, Hansen LK, Larsen J. Unveiling Music Structure Via PLSA Similarity Fusion. In 2007 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING. Formerly the IEEE Workshop on Neural Networks for Signal Processing, August 27-29, 2007, Thessaloniki, Greece. IEEE. 2007. p. 419-424. Available from: 10.1109/MLSP.2007.4414343

Author

Arenas-García, Jerónimo; Meng, Anders; Petersen, Kaare Brandt; Lehn-Schiøler, Tue; Hansen, Lars Kai; Larsen, Jan / Unveiling Music Structure Via PLSA Similarity Fusion.

2007 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING. Formerly the IEEE Workshop on Neural Networks for Signal Processing, August 27-29, 2007, Thessaloniki, Greece. IEEE, 2007. p. 419-424.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2007

Bibtex

@inbook{92ebcd54f76c41468fbe47625c08ed0f,
title = "Unveiling Music Structure Via PLSA Similarity Fusion",
publisher = "IEEE",
author = "Jerónimo Arenas-García and Anders Meng and Petersen, {Kaare Brandt} and Tue Lehn-Schiøler and Hansen, {Lars Kai} and Jan Larsen",
year = "2007",
doi = "10.1109/MLSP.2007.4414343",
isbn = "978-1-4244-1566-3",
pages = "419-424",
booktitle = "2007 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING. Formerly the IEEE Workshop on Neural Networks for Signal Processing, August 27-29, 2007, Thessaloniki, Greece",

}

RIS

TY - GEN

T1 - Unveiling Music Structure Via PLSA Similarity Fusion

A1 - Arenas-García,Jerónimo

A1 - Meng,Anders

A1 - Petersen,Kaare Brandt

A1 - Lehn-Schiøler,Tue

A1 - Hansen,Lars Kai

A1 - Larsen,Jan

AU - Arenas-García,Jerónimo

AU - Meng,Anders

AU - Petersen,Kaare Brandt

AU - Lehn-Schiøler,Tue

AU - Hansen,Lars Kai

AU - Larsen,Jan

PB - IEEE

PY - 2007

Y1 - 2007

N2 - Nowadays there is an increasing interest in developing methods for building music recommendation systems. In order to get a satisfactory performance from such a system, one needs to incorporate as much information about songs similarity as possible; however, how to do so is not obvious. In this paper, we build on the ideas of the Probabilistic Latent Semantic Analysis (PLSA) that has been successfully used in the document retrieval community. Under this probabilistic framework, any song will be projected into a relatively low dimensional space of "latent semantics", in such a way that that all observed similarities can be satisfactorily explained using the latent semantics. Additionally, this approach significantly simplifies the song retrieval phase, leading to a more practical system implementation. The suitability of the PLSA model for representing music structure is studied in a simplified scenario consisting of 10.000 songs and two similarity measures among them. The results suggest that the PLSA model is a useful framework to combine different sources of information, and provides a reasonable space for song representation.

AB - Nowadays there is an increasing interest in developing methods for building music recommendation systems. In order to get a satisfactory performance from such a system, one needs to incorporate as much information about songs similarity as possible; however, how to do so is not obvious. In this paper, we build on the ideas of the Probabilistic Latent Semantic Analysis (PLSA) that has been successfully used in the document retrieval community. Under this probabilistic framework, any song will be projected into a relatively low dimensional space of "latent semantics", in such a way that that all observed similarities can be satisfactorily explained using the latent semantics. Additionally, this approach significantly simplifies the song retrieval phase, leading to a more practical system implementation. The suitability of the PLSA model for representing music structure is studied in a simplified scenario consisting of 10.000 songs and two similarity measures among them. The results suggest that the PLSA model is a useful framework to combine different sources of information, and provides a reasonable space for song representation.

U2 - 10.1109/MLSP.2007.4414343

DO - 10.1109/MLSP.2007.4414343

SN - 978-1-4244-1566-3

BT - 2007 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING. Formerly the IEEE Workshop on Neural Networks for Signal Processing, August 27-29, 2007, Thessaloniki, Greece

T2 - 2007 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING. Formerly the IEEE Workshop on Neural Networks for Signal Processing, August 27-29, 2007, Thessaloniki, Greece

SP - 419

EP - 424

ER -