Comunicações C e T

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Percorrer Comunicações C e T por autor "Mendes, A. J."

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    Classification of Recorded Classical Music: a methodology and a comparative study
    (University of Stirling, 2004-09) Malheiro, Ricardo; Paiva, R. P.; Mendes, A. J.; Mendes, T.; Cardoso, A.
    As a result of recent technological innovations, there has been a tremendous growth in the Electronic Music Distribution industry. Consequently, tasks such as 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. We use the number of zero crossings, loudness, spectral centroid, bandwidth and uniformity for feature extraction. These features are statistically manipulated, making a total of 40 features. Regarding the task of genre modeling, we follow three approaches: the K-Nearest Neighbors (KNN) classifier, Gaussian Mixture Models (GMM) and feedforward neural networks (FFNN). A taxonomy of sub-genres 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 seek to discriminate between all five genres. The best results were obtained using FFNNs: 85% classification accuracy in the three-class problem, 90% in the two-class problem and 76% in the five-class problem. These results are encouraging and show that the presented methodology may be a good starting point for addressing more challenging tasks.
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    A Prototype for Classification of Classical Music Using Neural Networks
    (Proceedings of the Eighth IASTED International Conference, 2004-09) Malheiro, Ricardo; Paiva, Rui Pedro; Mendes, A. J.; Mendes, T.; Cardoso, A.
    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.