Automatic Sample Detection in Polyphonic Music

The term ‘sampling’ refers to the usage of snippets or loops from existing songs or sample libraries in new songs, mashups, or other music productions. The ability to automatically detect sampling in music is, for instance, beneficial for studies tracking artist influences geographically and temporally. We present a method based on Non-negative Matrix Factorization (NMF) and Dynamic Time Warping (DTW) for the automatic detection of a sample in a pool of songs. The method comprises of two processing steps: first, the DTW alignment path between NMF activations of a song and query sample is computed. Second, features are extracted from this path and used to train a Random Forest classifier to detect the presence of the sample. The method is able to identify samples that are pitch shifted and/or time stretched with approximately 63% F-measure. We evaluate this method against a new publicly available dataset of real-world sample and song pairs.