Classification of Recorded Classical Music: A Methodology and a Comparative Study



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 the number of zero crossings, loudness, spectral centroid, bandwidth and uniformity. 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 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.
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.


neural networks, music information retrieval, music classification, music signal analysis


Music Classification


International Symposium on Brain Inspired Cognitive Systems - BICS’2004, August 2004

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Year 2006 : 1 citations

 1. Tantini F. (2006). “Apprentissage d’automates stochastiques pour la classification automatique de styles musicaux”. Techinical Report, Jean Monnet University, Saint-Etiène, France.