Scalable Genre and Tag Prediction with Spectral Covariance

Cepstral analysis is effective in separating source from filter in vocal and monophonic [pitched] recordings, but is it a good general-purpose framework for working with music audio? We evaluate covariance in spectral features as an alternative to means and variances in cepstral features (particularly MFCCs) as summaries of frame-level features. We find that spectral covariance is more effective than mean, variance, and covariance statistics of MFCCs for genre and social tag prediction. Support for our model comes from strong and state-of-the-art performance on the GTZAN genre dataset, MajorMiner, and MagnaTagatune. Our classification strategy based on linear classifiers is easy to implement, exhibits very little sensitivity to hyper-parameters, trains quickly (even for web-scale datasets), is fast to apply, and offers competitive performance in genre and tag prediction.