Efficient Retrieval of Music Recordings Using Graph-Based Index Structures
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...
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doaj-ae43f03c94a548c890a0539749dc767a2021-06-01T00:14:16ZengMDPI AGSignals2624-61202021-05-0122133635210.3390/signals2020021Efficient Retrieval of Music Recordings Using Graph-Based Index StructuresFrank Zalkow0Julian Brandner1Meinard Müller2International Audio Laboratories Erlangen, 91058 Erlangen, GermanyInternational Audio Laboratories Erlangen, 91058 Erlangen, GermanyInternational Audio Laboratories Erlangen, 91058 Erlangen, GermanyFlexible 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 <i>K</i>-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.https://www.mdpi.com/2624-6120/2/2/21indexingmusic information retrievalnearest neighbor searchefficiencyruntime |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Frank Zalkow Julian Brandner Meinard Müller |
spellingShingle |
Frank Zalkow Julian Brandner Meinard Müller Efficient Retrieval of Music Recordings Using Graph-Based Index Structures Signals indexing music information retrieval nearest neighbor search efficiency runtime |
author_facet |
Frank Zalkow Julian Brandner Meinard Müller |
author_sort |
Frank Zalkow |
title |
Efficient Retrieval of Music Recordings Using Graph-Based Index Structures |
title_short |
Efficient Retrieval of Music Recordings Using Graph-Based Index Structures |
title_full |
Efficient Retrieval of Music Recordings Using Graph-Based Index Structures |
title_fullStr |
Efficient Retrieval of Music Recordings Using Graph-Based Index Structures |
title_full_unstemmed |
Efficient Retrieval of Music Recordings Using Graph-Based Index Structures |
title_sort |
efficient retrieval of music recordings using graph-based index structures |
publisher |
MDPI AG |
series |
Signals |
issn |
2624-6120 |
publishDate |
2021-05-01 |
description |
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 <i>K</i>-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. |
topic |
indexing music information retrieval nearest neighbor search efficiency runtime |
url |
https://www.mdpi.com/2624-6120/2/2/21 |
work_keys_str_mv |
AT frankzalkow efficientretrievalofmusicrecordingsusinggraphbasedindexstructures AT julianbrandner efficientretrievalofmusicrecordingsusinggraphbasedindexstructures AT meinardmuller efficientretrievalofmusicrecordingsusinggraphbasedindexstructures |
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1721415444477247488 |