Machine vision
Query by humming: musical information retrieval in an audio database
Proceedings of the third ACM international conference on Multimedia
Towards the digital music library: tune retrieval from acoustic input
Proceedings of the first ACM international conference on Digital libraries
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
A tool for content based navigation of music
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Content-based retrieval for music collections
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A practical query-by-humming system for a large music database
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Artificial Perception and Music Recognition
Artificial Perception and Music Recognition
Content-Based Classification, Search, and Retrieval of Audio
IEEE MultiMedia
Image Database Retrieval with Multiple-Instance Learning Techniques
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Music-listening systems
Peer-to-peer architecture for content-based music retrieval on acoustic data
WWW '03 Proceedings of the 12th international conference on World Wide Web
Warping indexes with envelope transforms for query by humming
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Indexing and matching of polyphonic songs for query-by-singing system
Proceedings of the 12th annual ACM international conference on Multimedia
QueST: querying music databases by acoustic and textual features
Proceedings of the 15th international conference on Multimedia
Compacting music signatures for efficient music retrieval
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Evaluation of similarity searching methods for music data in P2P networks
International Journal of Business Intelligence and Data Mining
Searching musical audio datasets by a batch of multi-variant tracks
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
IEICE - Transactions on Information and Systems
Local summarization and multi-level LSH for retrieving multi-variant audio tracks
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Combining multi-probe histogram and order-statistics based LSH for scalable audio content retrieval
Proceedings of the international conference on Multimedia
Similarity searching techniques in content-based audio retrieval via hashing
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Fast intra-collection audio matching
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
A survey of query-by-humming similarity methods
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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Content-based music retrieval research has mostly focused on symbolic data rather than acoustical data. Given that there are no general-purpose transcription algorithms that can convert acoustical data into musical scores, new methods are needed to do music retrieval on acoustical data. In this paper, we review some existing methods on contentbased music retrieval, discuss different definitions of music similarity, and present a new framework to perform music indexing and retrieval. The framework is based on an earlier prototype we developed, with significant improvements.In our framework known as MACSIS, each audio file is broken down into small segments and converted into feature vectors. All vectors are stored in a high-dimensional indexing structure called LSH, a probabilistic indexing scheme that makes use of multiple hashing instances in parallel. At retrieval time, small segments of audio matches are retrieved from the index and pieced together using the Hough Transform technique, and results are used as the basis to rank candidate matches.