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 appear as 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 train a feedforward neural network (FFNN) with the Levenberg-Marquardt algorithm. A taxonomy of subgenres of classical music is used. We consider three classification problems: in the first one, we aim to discriminate between music for flute, piano and violin; in the second problem, we distinguish choral music from opera; finally, in the third one, we aim to discriminate between all the abovementioned five genres together. We obtained 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