Style recognition through statistical event models
Bibliographic Data
| ID | 14872120 |
|---|---|
| Authors | Carlos Pérez-Sancho (0000-0003-1099-7417, University of Alicante, corresponding author), José Manuel Iñesta Quereda (0000-0001-6184-4694, University of Alicante), José M Iñesta, Jorge Calera-Rubio (University of Alicante) |
| Year | 2005 |
| Volume | 34 |
| Issue | 4 |
| Pages | 331-339 |
| Publication date | 2005-12-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of New Music Research (JOURNAL) |
| Journal identifiers | ISSN: 0929-8215 • E-ISSN: 1744-5027 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/09298210600578113 |
| OpenAlex | W2035149519 |
| Language | EN |
| References cited | 5 |
The automatic classification of music fragments into style classes is one challenging problem within the music information retrieval (MIR) domain and also for the understanding of music style perception. This has a number of applications, including the indexation and exploration of musical databases. Some technologies employed in text classification can be applied to this problem. The key point here is to establish the music equivalent to the words in texts. A number of works use the combination of intervals and duration ratios for this purpose. In this paper, different statistical text recognition algorithms are applied to style recognition using this kind of melody representation, exploring their performance for different word sizes
Linguistics · Music Information Retrieval · Musical · Natural language processing · Perception · Speech recognition · Computer Science · Mathematics · Music and Audio Processing · Psychology · Recommender Systems and Techniques · Time Series Analysis and Forecasting · Artificial Intelligence
| Highly cited | No |
|---|