Constrained nonnegative matrix factorization with applications to music transcription

Abstract

We apply nonnegative matrix factorization to the task of music transcription. In music transcription we are given an audio recording of a musical piece and attempt to find the underlying sheet music which generated the music. We improve upon current transcription results by imposing novel temporal and sparsity constraints which exploit the structure of music. We demonstrate the effectiveness or our technique of the MAPS dataset.