Decision time horizon for music genre classification using short time features
Peter Ahrendt, Anders Meng and Jan Larsen
In: EUSIPCO 2004, 6-10 Sep 2004, Vienna.
In this paper music genre classification has been explored with
special emphasis on the decision time horizon and ranking of tapped-delay-line
short-time features. Late information
fusion as e.g. majority voting is compared with techniques of
early information fusion (This term refers to the
decision making, i.e., early information fusion is an operation on the features
before classification (and decision making). This is opposed to late
information fusion (decision fusion) that assembles the information on the basis of the decisions) such as dynamic PCA (DPCA). The most
frequently suggested features in the literature were employed including
mel-frequency cepstral coefficients (MFCC), linear prediction coefficients (LPC), zero-crossing rate (ZCR), and MPEG-7 features.
To rank the importance of the
short time features consensus sensitivity analysis is applied.
A Gaussian classifier (GC) with full
covariance structure and a linear neural network (NN) classifier are used.