Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
The String-to-String Correction Problem
Journal of the ACM (JACM)
A Bayesian Approach to Key-Finding
ICMAI '02 Proceedings of the Second International Conference on Music and Artificial Intelligence
Robust Polyphonic Music Retrieval with N-grams
Journal of Intelligent Information Systems
Searching notated polyphonic music using transportation distances
Proceedings of the 12th annual ACM international conference on Multimedia
Tree model of symbolic music for tonality guessing
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Optimizations of local edition for evaluating similarity between monophonic musical sequences
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Toward a General Framework for Polyphonic Comparison
Fundamenta Informaticae - Special Issue on Stringology
Comparing approaches to the similarity of musical chord sequences
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
Toward a General Framework for Polyphonic Comparison
Fundamenta Informaticae - Special Issue on Stringology
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Estimating the symbolic music similarity is one of the major open problems in the music information retrieval research domain. Existing systems consider sequences of notes characterized by pitches and durations. Similarity estimation is mainly based on variations of pitches and durations and does not consider any other musical elements. However, musical elements such as tonality or rhythm are particularly important in the perception of music. In this paper we propose to investigate some algorithmic improvements that allow edit-based systems to take into account important musical elements: tonality, passing notes, strong and weak beats. These elements are illustrated with a few monophonic musical examples which lead to important errors in usual systems. First experiments with these examples show that the improvements induced are significant. Furthermore, experimental results obtained with the MIREX 2005 database are very good. All the results are thus very promising since they confirm that considering musical information improves the accuracy of music retrieval systems.