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
Musical information retrieval using melodic surface
Proceedings of the fourth ACM conference on Digital libraries
Visualizing music and audio using self-similarity
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Evaluation of a simple and effective music information retrieval method
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Robust Polyphonic Music Retrieval with N-grams
Journal of Intelligent Information Systems
Structural analysis of musical signals for indexing and thumbnailing
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Content-based music structure analysis with applications to music semantics understanding
Proceedings of the 12th annual ACM international conference on Multimedia
An architecture for effective music information retrieval
Journal of the American Society for Information Science and Technology - Music information retrieval
A phonotactic-semantic paradigm for automatic spoken document classification
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A Large-Scale Evaluation of Acoustic and Subjective Music-Similarity Measures
Computer Music Journal
A music search engine built upon audio-based and web-based similarity measures
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Searching for Music Using Natural Language Queries and Relevance Feedback
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
Local summarization and multi-level LSH for retrieving multi-variant audio tracks
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Audio retrieval by segment-based manifold-ranking
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Effectiveness of signal segmentation for music content representation
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Word level automatic alignment of music and lyrics using vocal synthesis
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Beat space segmentation and octave scale cepstral feature for sung language recognition in pop music
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Semantic annotation of digital music
Journal of Computer and System Sciences
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This paper proposes a novel framework for music content indexing and retrieval. The music structure information, i.e., timing, harmony and music region content, is represented by the layers of the music structure pyramid. We begin by extracting this layered structure information. We analyze the rhythm of the music and then segment the signal proportional to the inter-beat intervals. Thus, the timing information is incorporated in the segmentation process, which we call Beat Space Segmentation. To describe Harmony Events, we propose a two-layer hierarchical approach to model the music chords. We also model the progression of instrumental and vocal content as Acoustic Events. After information extraction, we propose a vector space modeling approach which uses these events as the indexing terms. In query-by-example music retrieval, a query is represented by a vector of the statistics of the n-gram events. We then propose two effective retrieval models, a hard-indexing scheme and a soft-indexing scheme. Experiments show that the vector space modeling is effective in representing the layered music information, achieving 82.5% top-5 retrieval accuracy using 15-sec music clips as the queries. The soft-indexing outperforms hard-indexing in general.