Multipitch Estimation of Piano Music by Exemplar-Based Sparse Representation


Pitch, together with other midlevel music features such as rhythm and timbre, holds the promise of bridging the semantic gap between low-level features and high-level semantics for music understanding. This paper investigates the pitch estimation of a piano music signal by exemplar-based sparse representation. A note exemplar is a segment of a piano note, stored in the dictionary. We first describe how to represent a segment of the piano music signal as a linear combination of a small number of note exemplars from a large note exemplar dictionary and then show how the sparse representation problem can be solved by $l_{1}$-regularized minimization. The proposed approach incorporates tuning factor estimation, note candidate selection, and hidden-Markov-model-based smoothing into the estimation process to improve accuracy. Unlike previous approaches, the proposed approach does not require retraining for a new piano. Instead, only a dozen notes of the new piano are needed. This feature is computationally attractive and avoids intense manual labeling. The system performance is evaluated using 70 classical music recordings of two real pianos under different recording conditions. The results show that the proposed system outperforms four state-of-the-art systems.

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