Name
Abstract | ||
|---|---|---|
In this paper we describe a musical stream note segmentation method that employs time-domain and frequency-domain analysis methods working in conjunction. The method has two demonstrated benefits: first, it leads to reliable results with very low probability of either missing or of falsely detecting notes, and second, it has temporal resolution on the order of 1 msec. We have applied the method to a variety of monophonic musical instrument recordings, including the clarinet, piano and violin, with results that vary from 95% to 100% accuracy. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/ICASSP.2004.1326817 | ICASSP '04). IEEE International Conference |
Keywords | Field | DocType |
audio signal processing,musical instruments,signal resolution,time-frequency analysis,clarinet,combined time frequency analyses,false detection probability,monophonic musical instrument recordings,musical stream note segmentation,piano,temporal resolution,violin | Pattern recognition,Musical,Segmentation,Computer science,Speech recognition,Violin,Musical instrument,Artificial intelligence,Piano,Musical note,Audio signal processing,Temporal resolution | Conference |
Volume | ISSN | ISBN |
4 | 1520-6149 | 0-7803-8484-9 |
Citations | PageRank | References |
3 | 1.20 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gordana Velikic | 1 | 10 | 8.37 |
| Titlebaum, Edward L. | 2 | 63 | 15.46 |
| Mark F. Bocko | 3 | 22 | 4.47 |