Name
Title | ||
|---|---|---|
Knowledge discovery and visualisation framework using machine learning for music information retrieval from broadcast radio data |
Abstract | ||
|---|---|---|
•We identify a lack of software frameworks for broadcast radio knowledge discovery.•A novel framework for this is proposed, using MIR and data mining technologies.•We compare radio stations via a novel SOM visualisation and similarity metric.•A method for building high-quality music datasets is proposed and demonstrated.•The use of the framework is analysed in multiple research and industry contexts. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.eswa.2021.115236 | Expert Systems with Applications |
Keywords | DocType | Volume |
Data mining,Machine learning,Sound and music computing,Signal processing systems,Software Architectures,Data and knowledge visualization,Record classification | Journal | 182 |
ISSN | Citations | PageRank |
0957-4174 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Furner | 1 | 0 | 0.34 |
| Md Zahidul Islam | 2 | 263 | 32.72 |
| Chang-Tsun Li | 3 | 24 | 5.11 |