Standard

A music recommendation system based on semantic audio segments similarity. / Bozzon, Alessandro; Prandi, Giorgio; Valenzise, Giuseppe; Tagliasacchi, Marco.

Proceedings of the IASTED International Conference on Internet and Multimedia Systems and Applications and Visual Communications. 2008. p. 182-187.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Bozzon, A, Prandi, G, Valenzise, G & Tagliasacchi, M 2008, A music recommendation system based on semantic audio segments similarity. in Proceedings of the IASTED International Conference on Internet and Multimedia Systems and Applications and Visual Communications. pp. 182-187, IASTED International Conference on Internet and Multimedia Systems and Applications and Visual Communications, Innsbruck, Austria, 17/03/08.

APA

Bozzon, A., Prandi, G., Valenzise, G., & Tagliasacchi, M. (2008). A music recommendation system based on semantic audio segments similarity. In Proceedings of the IASTED International Conference on Internet and Multimedia Systems and Applications and Visual Communications (pp. 182-187)

Vancouver

Bozzon A, Prandi G, Valenzise G, Tagliasacchi M. A music recommendation system based on semantic audio segments similarity. In Proceedings of the IASTED International Conference on Internet and Multimedia Systems and Applications and Visual Communications. 2008. p. 182-187

Author

Bozzon, Alessandro ; Prandi, Giorgio ; Valenzise, Giuseppe ; Tagliasacchi, Marco. / A music recommendation system based on semantic audio segments similarity. Proceedings of the IASTED International Conference on Internet and Multimedia Systems and Applications and Visual Communications. 2008. pp. 182-187

BibTeX

@inproceedings{b54aef87e0d842169213e7624219535e,
title = "A music recommendation system based on semantic audio segments similarity",
abstract = "In this paper we propose a novel approach for contentbased music recommendation. The main innovation of the proposed technique consists of a similarity function that, instead of considering entire songs or their thumbnail representations, analyzes audio similarities between semantic segments from different audio tracks. The rationale of our idea is that a song similarity and recommendation technique, to be more meaningful to the user from a semantic point of view, may evaluate and exploit similarities on semantic units between audio tracks. Our similarity algorithm consists of two main stages: the first step performs segmentation of the song in semantic parts. The latter assigns a similarity and recommendation score to a pair of songs, by computing the distance between the representations of their segments. To assign the global similarity and recommendation score, we consider a consistent subset of all the inter-segment distances. By adopting a graph-bases framework, we propose a graph-reduction algorithm on weighted edges that connect segments of different songs to optimize the similarity score with respect to our recommendation goal. Experiments conducted on a database of 200 audio tracks of various authors and genres show promising results.",
keywords = "Content-based multimedia retrieval, Genre classification, Music recommendation",
author = "Alessandro Bozzon and Giorgio Prandi and Giuseppe Valenzise and Marco Tagliasacchi",
year = "2008",
language = "English",
isbn = "978-088986727-7",
pages = "182--187",
booktitle = "Proceedings of the IASTED International Conference on Internet and Multimedia Systems and Applications and Visual Communications",

}

RIS

TY - GEN

T1 - A music recommendation system based on semantic audio segments similarity

AU - Bozzon, Alessandro

AU - Prandi, Giorgio

AU - Valenzise, Giuseppe

AU - Tagliasacchi, Marco

PY - 2008

Y1 - 2008

N2 - In this paper we propose a novel approach for contentbased music recommendation. The main innovation of the proposed technique consists of a similarity function that, instead of considering entire songs or their thumbnail representations, analyzes audio similarities between semantic segments from different audio tracks. The rationale of our idea is that a song similarity and recommendation technique, to be more meaningful to the user from a semantic point of view, may evaluate and exploit similarities on semantic units between audio tracks. Our similarity algorithm consists of two main stages: the first step performs segmentation of the song in semantic parts. The latter assigns a similarity and recommendation score to a pair of songs, by computing the distance between the representations of their segments. To assign the global similarity and recommendation score, we consider a consistent subset of all the inter-segment distances. By adopting a graph-bases framework, we propose a graph-reduction algorithm on weighted edges that connect segments of different songs to optimize the similarity score with respect to our recommendation goal. Experiments conducted on a database of 200 audio tracks of various authors and genres show promising results.

AB - In this paper we propose a novel approach for contentbased music recommendation. The main innovation of the proposed technique consists of a similarity function that, instead of considering entire songs or their thumbnail representations, analyzes audio similarities between semantic segments from different audio tracks. The rationale of our idea is that a song similarity and recommendation technique, to be more meaningful to the user from a semantic point of view, may evaluate and exploit similarities on semantic units between audio tracks. Our similarity algorithm consists of two main stages: the first step performs segmentation of the song in semantic parts. The latter assigns a similarity and recommendation score to a pair of songs, by computing the distance between the representations of their segments. To assign the global similarity and recommendation score, we consider a consistent subset of all the inter-segment distances. By adopting a graph-bases framework, we propose a graph-reduction algorithm on weighted edges that connect segments of different songs to optimize the similarity score with respect to our recommendation goal. Experiments conducted on a database of 200 audio tracks of various authors and genres show promising results.

KW - Content-based multimedia retrieval

KW - Genre classification

KW - Music recommendation

UR - http://www.scopus.com/inward/record.url?scp=62649123726&partnerID=8YFLogxK

M3 - Conference contribution

SN - 978-088986727-7

SP - 182

EP - 187

BT - Proceedings of the IASTED International Conference on Internet and Multimedia Systems and Applications and Visual Communications

ER -

ID: 36755409