Constrained non-negative sparse coding using learnt instrument templates for realtime music transcription

In this paper, we present a realtime signal decomposition method with single-pitch and harmonicity constrains using instrument specific information. Although the proposed method is designed for monophonic music transcription, it can be used as a candidate selection technique in combination with other realtime transcription methods to address polyphonic signals. The harmonicity constraint is particularly beneficial for automatic transcription because, in this way, each basis can define a single pitch. Furthermore, restricting the model to have a single-nonzero gain at each frame has been shown to be a very suitable constraint when dealing with monophonic signals. In our method, both harmonicity and single-nonzero gain constraints are enforced in a deterministic manner. A realtime factorization procedure based on Non-negative sparse coding (NNSC) with Beta-divergence and fixed basis functions is proposed. In this paper, the basis functions are learned using a supervised process to obtain spectral patterns for different musical instruments. The proposed method has been tested for music transcription of both monophonic and polyphonic signals and has been compared with other state-of-the-art transcription methods, and in these tests, the proposed method has obtained satisfactory results in terms of accuracy and runtime.