Improving Music Genre Classification by Short Time Feature Integration | Anders Meng, Peter Ahrendt, Jan Larsen
| | Abstract | Many different short-time
features (derived from 10-30ms of audio) have been proposed for
music segmentation, retrieval and genre classification.
Often the available time frame of the music to make a
decision (the decision time horizon) is in the range of seconds
instead of milliseconds.
The problem of making new features on the larger time scale
from the short-time features (feature integration) has only
received little attention.
This paper investigates different methods for feature
integration (early information fusion) and late
information fusion (assembling of probabilistic outputs or
decisions from the classifier, e.g. majority voting) for music
genre classification. | | Keywords | Music genre, Autoregressive Model, Information Fusion | | Type | Misc [Poster] | | Journal/Book/Conference | ICASSP | | Year | 2005 Month March | | Electronic version(s) | [pdf] | | BibTeX data | [bibtex] | | IMM Group(s) | Intelligent Signal Processing |
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