Piano multipitch estimation using sparse coding embedded deep learning

Abstract

As the foundation of many applications, multipitch estimation problem has always been the focus of acoustic music processing; however, existing algorithms perform deficiently due to its complexity. In this paper, we employ deep learning to address piano multipitch estimation problem by proposing MPENet based on a novel multimodal sparse incoherent non-negative matrix factorization (NMF) layer. This layer originates from a multimodal NMF problem with Lorentzian-BlockFrobenius sparsity constraint and incoherentness regularization. Experiments show that MPENet achieves state-of-the-art performance (83.65% F-measure for polyphony level 6) on RAND subset of MAPS dataset. MPENet enables NMF to do online learning and accomplishes multi-label classification by using only monophonic samples as training data. In addition, our layer algorithms can be easily modified and redeveloped for a wide variety of problems.

Wavelet transform-based multipitch estimation in polyphonic music

“…A method for estimating the fundamental frequencies of several concurrent sounds in polyphonic music and multiple-speaker speech signals is presented in [2]. The estimation of multiple concurrent pitches in piano recordings is presented in [3,4,5,6] and the piano multipitch estimation using sparse coding embedded deep learning is presented in [7]. Polyphonic transcription method converts a music audio signal into a human-readable musical score by integrating multi-pitch detection and rhythm quantization methods [8].…”