Autor(es):
Malheiro, Ricardo 
; Paiva, R. P. ; Mendes, A. J. ; Mendes, T. ; Cardoso, A.
Data: 2004
Origem: Repositório Aberto do Instituto Superior Miguel Torga
Assunto(s): neural networks
Detalhes do Documento
A prototype for classification of classical music using neural networks
Descrição
As a result of recent technological innovations, there has been a tremendous growth in the Electronic Music Distribution industry. In this way, tasks such us automatic music genre classification address new and exciting research challenges. Automatic music genre recognition involves issues like feature extraction and development of classifiers using the obtained features. As for feature extraction, we use features such as the number of zero crossings, loudness, spectral centroid, bandwidth and uniformity. These are statistically manipulated, making a total of 40 features. As for the task of genre modeling, we train a feedforward neural network (FFNN). A taxonomy of subgenres of classical music is used. We consider three classification problems: in the first one, we aim at discriminating between music for flute, piano and violin; in the second problem, we distinguish choral music from opera; finally, in the third one, we aim at discriminating between all five genres. Preliminary results are presented and discussed, which show that the presented methodology may be a good starting point for addressing more challenging tasks, such as using a broader range of musical categories.
As a result of recent technological innovations, there has been a tremendous growth in the Electronic Music Distribution industry. In this way, tasks such us automatic music genre classification address new and exciting research challenges. Automatic music genre recognition involves issues like feature extraction and development of classifiers using the obtained features. As for feature extraction, we use features such as the number of zero crossings, loudness, spectral centroid, bandwidth and uniformity. These are statistically manipulated, making a total of 40 features. As for the task of genre modeling, we train a feedforward neural network (FFNN). A taxonomy of subgenres of classical music is used. We consider three classification problems: in the first one, we aim at discriminating between music for flute, piano and violin; in the second problem, we distinguish choral music from opera; finally, in the third one, we aim at discriminating between all five genres. Preliminary results are presented and discussed, which show that the presented methodology may be a good starting point for addressing more challenging tasks, such as using a broader range of musical categories.
Tipo de Documento
Palestra
Idioma Inglês
Idioma Inglês
| Financiadores do RCAAP | |||||||
|
|
|
|
|
|
||