Machine vision
Efficient acoustic index for music retrieval with various degrees of similarity
Proceedings of the tenth ACM international conference on Multimedia
Audio Indexing for Efficient Music Information Retrieval
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Music structure based vector space retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploring composite acoustic features for efficient music similarity query
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Multi-probe LSH: efficient indexing for high-dimensional similarity search
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Using Exact Locality Sensitive Mapping to Group and Detect Audio-Based Cover Songs
ISM '08 Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia
IEICE - Transactions on Information and Systems
Chroma Binary Similarity and Local Alignment Applied to Cover Song Identification
IEEE Transactions on Audio, Speech, and Language Processing
Audio thumbnailing of popular music using chroma-based representations
IEEE Transactions on Multimedia
Combining multi-probe histogram and order-statistics based LSH for scalable audio content retrieval
Proceedings of the international conference on Multimedia
Scalable discovery of best clusters on large graphs
Proceedings of the VLDB Endowment
A query by humming system based on locality sensitive hashing indexes
Signal Processing
Edge-based locality sensitive hashing for efficient geo-fencing application
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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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.