Music Classification Using Kolmogorov Distance

Abstract. We evaluate the music composer classification using an approximation of the Kolmogorov distance between different music pieces. The distance approximation has recently been suggested by Vitanyi and his colleagues. They use a clustering method to evalute the distance metric. However the clustering is too slow for large (>60) data sets. We suggest using the distance metric together with a k-nearest neighbor classifier. We measure the performance of the distance metric based on the test classification accuracy of the classifier. A classification accuracy of 79% is achieved for a training data set of 57 midi files from three different classical composers. We find out that the classification accuracy increases with training set size. The performance of the metric seems to also depend on different pre-processing methods, hence domain knowledge and input representation could make a difference on how the distance metric performs.