Local summarization and multi-level LSH for retrieving multi-variant audio tracks

Document Type

Conference Proceeding

Publication Date

12-28-2009

Abstract

In this paper we study the problem of detecting and grouping multi-variant audio tracks in large audio datasets. To address this issue, a fast and reliable retrieval method is necessary. But reliability requires elaborate representations of audio content, which challenges fast retrieval by similarity from a large audio database. To find a better tradeoff between retrieval quality and efficiency, we put forward an approach relying on local summarization and multi-level Locality-Sensitive Hashing (LSH). More precisely, each audio track is divided into multiple Continuously Correlated Periods (CCP) of variable length according to spectral similarity. The description for each CCP is calculated based on its Weighted Mean Chroma (WMC). A track is thus represented as a sequence of WMCs. Then, an adapted two-level LSH is employed for efficiently delineating a narrow relevant search region. The "coarse" hashing level restricts search to items having a non-negligible similarity to the query. The subsequent, "refined" level only returns items showing a much higher similarity. Experimental evaluations performed on a real multi-variant audio dataset confirm that our approach supports fast and reliable retrieval of audio track variants. Copyright 2009 ACM.

Identifier

72449178943 (Scopus)

ISBN

[9781605586083]

Publication Title

Mm 09 Proceedings of the 2009 ACM Multimedia Conference with Co Located Workshops and Symposiums

External Full Text Location

https://doi.org/10.1145/1631272.1631320

First Page

341

Last Page

350

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