Name
Abstract | ||
|---|---|---|
In recent years Hidden Markov Models (HMMs) have been successfully applied to human speech recognition. The present article proves that this technique is also valid to detect musical characteristics, for example: musical notes. However, any recognition system needs to get a suitable set of parameters, that is, a reduced set of magnitudes that represent the outstanding aspects to classify an entity. This paper shows how a suitable parameterisation and adequate HMMs topology make a robust recognition system of musical notes. At the same time, the way to extract parameters can be used in other recognition technologies applied to music. |
Year | Venue | Keywords |
|---|---|---|
2007 | SIGMAP 2007: PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND MULTIMEDIA APPLICATIONS | music information retrieval,Hidden Markov Models,music features extraction,music notes recognitio |
Field | DocType | Citations |
Maximum-entropy Markov model,Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Hidden Markov model,Viterbi algorithm | Conference | 0 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fco. Javier Salcedo | 1 | 0 | 0.34 |
| Jesús E. Díaz-Verdejo | 2 | 219 | 20.22 |
| José C. Segura | 3 | 481 | 38.14 |