Instrument Learning and Sparse NMD for Automatic Polyphonic Music Transcription

Abstract

In this paper, an automatic music transcription (AMT) algorithm based on a supervised non-negative 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 $\ell _2$ regularization and a threshold filtering. In the first step, the NMD is performed with the $\ell _2$ regularization in order to get an overall selection of the notes most likely appearing in the monotimbral musical excerpt. In the second step, a threshold filtering followed by another $\ell _2$ regularized NMD are repeatedly performed in order to progressively reduce the dictionary matrix and to refine the notes transcription. Furthermore, a user-oriented instrument learning procedure has been conceived and proposed. The proposed AMT system has been tested upon the dataset collected by the LabROSA laboratories considering the transcription of three different pianos. Moreover, it has been validated through a comparison with a regularized NMD and with three open source AMT software. The results prove the effectiveness of the proposed two-step processing in enhancing the sparsity of the solution and in improving the transcription accuracy. Moreover, the proposed system shows promising performance in both multi-F0 and note tracking tasks, obtaining better transcription accuracy than the competing algorithms in most tests.