TY - GEN
T1 - Combining multi-probe histogram and order-statistics based LSH for scalable audio content retrieval
AU - Yu, Yi
AU - Crucianu, Michel
AU - Oria, Vincent
AU - Damiani, Ernesto
PY - 2010
Y1 - 2010
N2 - In order to improve the reliability and the scalability of content-based retrieval of variant audio tracks from large music databases, we suggest a new multi-stage LSH scheme that consists in (i) extracting compact but accurate representations from audio tracks by exploiting the LSH idea to summarize audio tracks, and (ii) adequately organizing the resulting representations in LSH tables, retaining almost the same accuracy as an exact kNN retrieval. In the first stage, we use major bins of successive chroma features to calculate a multi-probe histogram (MPH) that is concise but retains the information about local temporal correlations. In the second stage, based on the order statistics (OS) of the MPH, we propose a new LSH scheme, OS-LSH, to organize and probe the histograms. The representation and organization of the audio tracks are storage efficient and support robust and scalable retrieval. Extensive experiments over a large dataset with 30,000 real audio tracks confirm the effectiveness and efficiency of the proposed scheme.
AB - In order to improve the reliability and the scalability of content-based retrieval of variant audio tracks from large music databases, we suggest a new multi-stage LSH scheme that consists in (i) extracting compact but accurate representations from audio tracks by exploiting the LSH idea to summarize audio tracks, and (ii) adequately organizing the resulting representations in LSH tables, retaining almost the same accuracy as an exact kNN retrieval. In the first stage, we use major bins of successive chroma features to calculate a multi-probe histogram (MPH) that is concise but retains the information about local temporal correlations. In the second stage, based on the order statistics (OS) of the MPH, we propose a new LSH scheme, OS-LSH, to organize and probe the histograms. The representation and organization of the audio tracks are storage efficient and support robust and scalable retrieval. Extensive experiments over a large dataset with 30,000 real audio tracks confirm the effectiveness and efficiency of the proposed scheme.
KW - audio computing
KW - locality sensitive hashing
KW - multi-probe histogram
KW - music-IR
KW - order statistics
KW - variant audio search
UR - http://www.scopus.com/inward/record.url?scp=78650991451&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650991451&partnerID=8YFLogxK
U2 - 10.1145/1873951.1874004
DO - 10.1145/1873951.1874004
M3 - Conference contribution
AN - SCOPUS:78650991451
SN - 9781605589336
T3 - MM'10 - Proceedings of the ACM Multimedia 2010 International Conference
SP - 381
EP - 390
BT - MM'10 - Proceedings of the ACM Multimedia 2010 International Conference
T2 - 18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10
Y2 - 25 October 2010 through 29 October 2010
ER -