Wavelet transform-based multipitch estimation in polyphonic music

Kumar, Neeraj; Kumar, Raubin

doi:10.1016/j.heliyon.2020.e03243

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…Su et al [19] proposed to combine spectral information with temporal representations to refine the MPE for polyphonic music. More recent methods for MPE propose the use of modified wavelet transform to improve the overall performance in polyphonic music [20]. These are scenarios where explicit LPE could prove helpful, as it may help by providing a search scope and an upper boundary during musical sections with strong spectral overlap of the instruments and notes.…”

Section: Related Workmentioning

confidence: 99%

Informing Piano Multi-Pitch Estimation with Inferred Local Polyphony Based on Convolutional Neural Networks

2021

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In this work, we propose considering the information from a polyphony for multi-pitch estimation (MPE) in piano music recordings. To that aim, we propose a method for local polyphony estimation (LPE), which is based on convolutional neural networks (CNNs) trained in a supervised fashion to explicitly predict the degree of polyphony. We investigate two feature representations as inputs to our method, in particular, the Constant-Q Transform (CQT) and its recent extension Folded-CQT (F-CQT). To evaluate the performance of our method, we conduct a series of experiments on real and synthetic piano recordings based on the MIDI Aligned Piano Sounds (MAPS) and the Saarland Music Data (SMD) datasets. We compare our approaches with a state-of-the art piano transcription method by informing said method with the LPE knowledge in a postprocessing stage. The experimental results suggest that using explicit LPE information can refine MPE predictions. Furthermore, it is shown that, on average, the CQT representation is preferred over F-CQT for LPE.

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Section: Related Workmentioning

confidence: 99%

Informing Piano Multi-Pitch Estimation with Inferred Local Polyphony Based on Convolutional Neural Networks

2021

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“…Over the past few years, a plethora of approaches have been taken to tackle the challenge of pitch estimation, with methods utilizing the wavelet transform (Kumar and Kumar 2020), twodimensional spectra (Zhang, Chen, and Yin 2020), and visual information (i.e. by viewing the physical instrument itself) (Koepke, Wiles, and Zisserman 2019), amongst others.…”

Section: Related Workmentioning

confidence: 99%

A geometric framework for pitch estimation on acoustic musical signals

Goodman

Gemst

Tiňo

2021

Journal of Mathematics and Music

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This paper presents a geometric approach to pitch estimation (PE) -an important problem in music information retrieval (MIR), and a precursor to a variety of other problems in the field. Though there exist a number of highly accurate methods, both mono-pitch estimation and multi-pitch estimation (particularly with unspecified polyphonic timbre) prove computationally and conceptually challenging. A number of current techniques, while incredibly effective, are not targeted towards eliciting the underlying mathematical structures that underpin the complex musical patterns exhibited by acoustic musical signals. Tackling the approach from both theoretical and experimental perspectives, we present a novel framework, a basis for further work in the area, and results that (while not state of the art) demonstrate relative efficacy. The framework presented in this paper opens up a completely new way to tackle PE problems and may have uses both in traditional analytical approaches as well as in the emerging machine learning (ML) methods that currently dominate the literature.

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“…Over the past few years, a plethora of approaches have been taken to tackle the challenge of pitch estimation -with methods utilising the wavelet transform (Kumar and Kumar 2020), two-dimensional spectra (Zhang, Chen, and Yin 2020), and visual infomation (i.e. by viewing the physical instrument itself) (Koepke, Wiles, and Zisserman 2019), amongst others.…”

Section: Related Workmentioning

confidence: 99%

A Geometric Framework for Pitch Estimation on Acoustic Musical Signals

Goodman,

van Gemst,

Tino

2020

Preprint

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This paper presents a geometric approach to pitch estimation (PE) -an important problem in Music Information Retrieval (MIR), and a precursor to a variety of other problems in the field. Though there exist a number of highly-accurate methods, both mono-pitch estimation and multi-pitch estimation (particularly with unspecified polyphonic timbre) prove computationally and conceptually challenging. A number of current techniques, whilst incredibly effective, are not targeted towards eliciting the underlying mathematical structures that underpin the complex musical patterns exhibited by acoustic musical signals. Tackling the approach from both a theoretical and experimental perspective, we present a novel framework, a basis for further work in the area, and results that (whilst not state of the art) demonstrate relative efficacy. The framework presented in this paper opens up a completely new way to tackle PE problems, and may have uses both in traditional analytical approaches, as well as in the emerging machine learning (ML) methods that currently dominate the literature.

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Wavelet transform-based multipitch estimation in polyphonic music

Cited by 8 publications

References 19 publications

Informing Piano Multi-Pitch Estimation with Inferred Local Polyphony Based on Convolutional Neural Networks

Informing Piano Multi-Pitch Estimation with Inferred Local Polyphony Based on Convolutional Neural Networks

A geometric framework for pitch estimation on acoustic musical signals

A Geometric Framework for Pitch Estimation on Acoustic Musical Signals

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