Music Motive Extraction Through Hanson Intervallic Analysis

Serrano, Johnny; Iñesta, José M.

doi:10.1109/cic.2006.48

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…MIR has gradually developed into an interdisciplinary research field that is related to audio signal processing [8,9], audio content indexing [1,2,3,4,7,10], sequence matching [8], pattern recognition [11], music retrieval evaluation [12], etc. Applications of queryby-audio MIR include near duplicate audio detection [13], audio-based music plagiarism analysis [14] and query-byexample/humming/singing [1,10]. Although different issues such as search intention, music genre and mood, personal interests and culture background, have to be considered during the search formulation, the retrieval process is generally simple: take audio content as the query, perform similarity search and finally return the results in a ranked list.…”

Section: Background and Related Workmentioning

confidence: 99%

Combining multi-probe histogram and order-statistics based LSH for scalable audio content retrieval

Yu¹,

Crucianu²,

Oria³

et al. 2010

Proceedings of the 18th ACM International Conference on Multimedia

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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.

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