Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Efficient acoustic index for music retrieval with various degrees of similarity
Proceedings of the tenth ACM international conference on Multimedia
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Warping indexes with envelope transforms for query by humming
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Exact indexing of dynamic time warping
Knowledge and Information Systems
Information Retrieval for Music and Motion
Information Retrieval for Music and Motion
Towards timbre-invariant audio features for harmony-based music
IEEE Transactions on Audio, Speech, and Language Processing
Embedding-based subsequence matching in time-series databases
ACM Transactions on Database Systems (TODS)
Analysis of Minimum Distances in High-Dimensional Musical Spaces
IEEE Transactions on Audio, Speech, and Language Processing
Efficient Index-Based Audio Matching
IEEE Transactions on Audio, Speech, and Language Processing
International Journal on Digital Libraries - Focused Issue on Music Digital Libraries
Proceedings of the 20th ACM international conference on Multimedia
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The general goal of audio matching is to identify all audio extracts of a music collection that are similar to a given query snippet. Over the last years, several approaches to this task have been presented. However, due to the complexity of audio matching the proposed approaches usually either yield excellent matches but have a poor runtime or provide quick responses albeit calculate less satisfying retrieval results. In this paper, we present a novel procedure that combines the positive aspects and efficiently computes good retrieval results. Our idea is to exploit the fact that in some practical applications queries are not arbitrary audio snippets but are rather given as extracts from the music collection itself (intra-collection query). This allows us to split the audio collection into equal sized overlapping segments and to precompute their retrieval results using dynamic time warping (DTW). Storing these matches in appropriate index structures enables us to efficiently recombine them at runtime. Our experiments indicate a significant speedup compared to classical DTW-based audio retrieval while achieving nearly the same retrieval quality.