Instrument learning and sparse NMD for automatic polyphonic music transcription
In this paper, an Automatic Music Transcription (AMT) algorithm based on a supervised Non-negatve Matrix Decomposition (NMD) is discussed. In particular, a novel approach for enhancing the sparsity of the solution is proposed. It consists of a two-step processing in which the NMD is solved joining a `2 regularization and a threshold filtering. In the first step, the NMD is performed with the `2 regularization in order to get an overall selection of the notes most likely appearing in the monotimbral musical excerpt.