  In this paper, we proposed a robust music genre classification method based
on a sparse FFT based feature extraction method which extracted with
discriminating power of spectral analysis of non-stationary audio signals, and
the capability of sparse representation based classifiers. Feature extraction
method combines two sets of features namely short-term features (extracted from
windowed signals) and long-term features (extracted from combination of
extracted short-time features). Experimental results demonstrate that the
proposed feature extraction method leads to a sparse representation of audio
signals. As a result, a significant reduction in the dimensionality of the
signals is achieved. The extracted features are then fed into a sparse
representation based classifier (SRC). Our experimental results on the GTZAN
database demonstrate that the proposed method outperforms the other state of
the art SRC approaches. Moreover, the computational efficiency of the proposed
method is better than that of the other Compressive Sampling (CS)-based
classifiers.
