TY - GEN
T1 - Fusing web and audio predictors to localize the origin of music pieces for geospatial retrieval
AU - Schedl, Markus
AU - Zhou, Fang
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Localizing the origin of a music piece around the world enables some interesting possibilities for geospatial music retrieval, for instance, location-aware music retrieval or recommendation for travelers or exploring non-Western music – a task neglected for a long time in music information retrieval (MIR). While previous approaches for the task of determining the origin of music either focused solely on exploiting the audio content or web resources, we propose a method that fuses features from both sources in a way that outperforms standalone approaches. To this end, we propose the use of block-level features inferred from the audio signal to model music content. We show that these features outperform timbral and chromatic features previously used for the task. On the other hand, we investigate a variety of strategies to construct web-based predictors from web pages related to music pieces. We assess different parameters for this kind of predictors (e.g., number of web pages considered) and define a confidence threshold for prediction. Fusing the proposed audio-and web-based methods by a weighted Borda rank aggregation technique, we show on a previously used dataset of music from 33 countries around the world that the median placing error can be reduced from 1,815 to 0 kilometers using K-nearest neighbor regression.
AB - Localizing the origin of a music piece around the world enables some interesting possibilities for geospatial music retrieval, for instance, location-aware music retrieval or recommendation for travelers or exploring non-Western music – a task neglected for a long time in music information retrieval (MIR). While previous approaches for the task of determining the origin of music either focused solely on exploiting the audio content or web resources, we propose a method that fuses features from both sources in a way that outperforms standalone approaches. To this end, we propose the use of block-level features inferred from the audio signal to model music content. We show that these features outperform timbral and chromatic features previously used for the task. On the other hand, we investigate a variety of strategies to construct web-based predictors from web pages related to music pieces. We assess different parameters for this kind of predictors (e.g., number of web pages considered) and define a confidence threshold for prediction. Fusing the proposed audio-and web-based methods by a weighted Borda rank aggregation technique, we show on a previously used dataset of music from 33 countries around the world that the median placing error can be reduced from 1,815 to 0 kilometers using K-nearest neighbor regression.
UR - https://www.scopus.com/pages/publications/84962604125
U2 - 10.1007/978-3-319-30671-1_24
DO - 10.1007/978-3-319-30671-1_24
M3 - 会议稿件
AN - SCOPUS:84962604125
SN - 9783319306704
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 322
EP - 334
BT - Advances in Information Retrieval - 38th European Conference on IR Research, ECIR 2016, Proceedings
A2 - Moens, Marie-Francine
A2 - Ferro, Nicola
A2 - Silvello, Gianmaria
A2 - di Nunzio, Giorgio Maria
A2 - Hauff, Claudia
A2 - Crestani, Fabio
A2 - Mothe, Josiane
A2 - Silvestri, Fabrizio
PB - Springer Verlag
T2 - 38th European Conference on Information Retrieval Research, ECIR 2016
Y2 - 20 March 2016 through 23 March 2016
ER -