Name
Abstract | ||
|---|---|---|
Demand for music databases is increasing for the stud- ies of musicology and music informatics. Our goal is to construct databases that contain deviations of tempo, and dynamics, start-timing, and duration of each note. This paper describes a procedure based on hybrid use of DP Matching and HMM that efficiently extracts deviations fromMIDI-formattedexpressive human performances. The algorithm of quantizing the start-timing of the notes has been successfully tested on a database of ten expressive piano performances. It gives an accuracy of 92.9% when one note per bar is given as the guide. This paper also in- troduces tools provided so that the public can make use of our database on the web. Keywords Database, HMM, DP matching |
Year | Venue | Keywords |
|---|---|---|
2004 | ISMIR 2013 | human performance |
Field | DocType | Citations |
Music informatics,Computer science,Musicology,Piano,Artificial intelligence,Quantization (signal processing),Hidden Markov model,Utility system,Database,Machine learning | Conference | 2 |
PageRank | References | Authors |
0.62 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ken'ichi Toyoda | 1 | 2 | 0.62 |
| Kenzi Noike | 2 | 6 | 2.32 |
| Haruhiro Katayose | 3 | 259 | 48.73 |