Name
Abstract | ||
|---|---|---|
Melody extraction from polyphonic music is a valuable but difficult problem in music information retrieval. The extraction incurs a large computational cost that limits its application. Growing processing cores and increased bandwidth have made GPU an ideal candidate for the development of fine-grained parallel algorithms. In this paper, we present a parallel approach for salience-based melody extraction from polyphonic music using CUDA. For 21 seconds of polyphonic clip, the extraction time is cut from 3 seconds to 33 milliseconds using NVIDIA GeForce GTX 480 which is up to 100 times faster. The increased performance allows the melody extraction to be carried out for real-time applications. Furthermore, the evaluation of the extraction on huge datasets is also possible. We give insight into how such significant speed gains are made and encourage the development and adoption of GPU in music information retrieval field. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-40820-5_24 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Vocal Melody,Music information retrieval,Computer science,CUDA,Parallel algorithm,Speech recognition,Bandwidth (signal processing),Non-negative matrix factorization,Polyphony | Conference | 8147 |
Issue | ISSN | Citations |
null | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 13 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guangchao Yao | 1 | 4 | 2.13 |
| Yao Zheng | 2 | 0 | 0.34 |
| Limin Xiao | 3 | 231 | 47.05 |
| Li Ruan | 4 | 123 | 25.10 |
| Zhen Lin | 5 | 0 | 0.34 |
| Junjie Peng | 6 | 40 | 14.41 |