Causal Prediction of Continuous Valued Music Features
This paper investigates techniques for predicting sequences of continuous valued feature vectors extracted from musical audio. In particular, we consider prediction of beatsynchronousMel frequencycepstralcoefficientsandchroma features in a causal setting, where features are predicted as they unfold in time. The methods studied comprise autoregressive models, N gram models incorporating a smoothing scheme, and a novel technique based on repetition detection using a self distance matrix. Furthermore, we propose a method for combining predictors, which relies on a running estimate of the error variance of the predictors to inform a linear weighting of the predictor outputs. Results indicate that incorporating information on long term structure improves the prediction performance for continuous valued, sequential musical data. For the Beatles data set, combining the proposed self distance based predictor with both N gram and autoregressive methods results in an average of 13% improvement compared to a linear predictive baseline.
ISMIR