Zalkow F, Brandner J, Müller M (2021)
Publication Type: Journal article
Publication year: 2021
Book Volume: 2
Pages Range: 336-352
Issue: 2
Journal Issue: 2
Flexible retrieval systems are required for conveniently browsing through large music collections. In a particular content-based music retrieval scenario, the user provides a query audio snippet, and the retrieval system returns music recordings from the collection that are similar to the query. In this scenario, a fast response from the system is essential for a positive user experience. For realizing low response times, one requires index structures that facilitate efficient search operations. One such index structure is the K-d tree, which has already been used in music retrieval systems. As an alternative, we propose to use a modern graph-based index, denoted as Hierarchical Navigable Small World (HNSW) graph. As our main contribution, we explore its potential in the context of a cross-version music retrieval application. In particular, we report on systematic experiments comparing graph- and tree-based index structures in terms of the retrieval quality, disk space requirements, and runtimes. Despite the fact that the HNSW index provides only an approximate solution to the nearest neighbor search problem, we demonstrate that it has almost no negative impact on the retrieval quality in our application. As our main result, we show that the HNSW-based retrieval is several orders of magnitude faster. Furthermore, the graph structure also works well with high-dimensional index items, unlike the tree-based structure. Given these merits, we highlight the practical relevance of the HNSW graph for music information retrieval (MIR) applications.
APA:
Zalkow, F., Brandner, J., & Müller, M. (2021). Efficient Retrieval of Music Recordings Using Graph-Based Index Structures. Signals, 2(2), 336-352. https://dx.doi.org/10.3390/signals2020021
MLA:
Zalkow, Frank, Julian Brandner, and Meinard Müller. "Efficient Retrieval of Music Recordings Using Graph-Based Index Structures." Signals 2.2 (2021): 336-352.
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