Automatic classification of music genres is an inherent field of music information retrieval research. Nearly all state-of-the-art music genre recognition systems start from the feature extraction block. The extracted acoustical features often could be correlated or/and redundant, which can course various difficulties on the classification stage. In this paper we present a comparative analysis on applying supervised Feature Selection and Feature Space Transformation algorithms to reduce the feature dimensionality. We discuss pro and contra of the methods and weigh the benefits of each one against the others.
Click to purchase paper as a non-member or you can login as an AES member to see more options.
No AES members have commented on this paper yet.
To be notified of new comments on this paper you can
subscribe to this RSS feed.
Forum users should login to see additional options.
If you are not yet an AES member and have something important to say about this paper then we urge you to join the AES today and make your voice heard. You can join online today by clicking here.
