Multiple F0 Estimation and Source Clustering of Polyphonic Music Audio Using PLCA and HMRFs

Arora, Vipul; Behera, Laxmidhar

doi:10.1109/taslp.2014.2387388

Cited by 26 publications

(12 citation statements)

References 28 publications

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“…Among them, short-term continuity is more effective in detecting high-pitched music signals. Related scholars have proposed a semiblind separation of speech and music based on sparsity and continuity; they used sparsity and continuity constraints to optimize dictionary coefficients, used the dictionary to represent the power spectral density of each source signal, and mixed them through a nonlinear function [24][25][26][27][28][29][30][31][32]. e power spectrum of the signal is mapped to the dictionary space, and finally, the source signal is reconstructed using an adaptive Wiener filter and spectral subtraction.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Research on Segmentation Experience of Music Signal Improved Based on Maximization of Negative Entropy

Qin

2021

Complexity

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With the rapid growth of digital music today, due to the complexity of the music itself, the ambiguity of the definition of music category, and the limited understanding of the characteristics of human auditory perception, the research on topics related to automatic segmentation of music is still in its infancy, while automatic music is still in its infancy. Segmentation is a prerequisite for fast and effective retrieval of music resources, and its potential application needs are huge. Therefore, topics related to automatic music segmentation have important research value. This paper studies an improved algorithm based on negative entropy maximization for well-posed speech and music separation. Aiming at the problem that the separation performance of the negative entropy maximization method depends on the selection of the initial matrix, the Newton downhill method is used instead of the Newton iteration method as the optimization algorithm to find the optimal matrix. By changing the descending factor, the objective function shows a downward trend, and the dependence of the algorithm on the initial value is reduced. The simulation experimental results show that the algorithm can separate the source signal well under different initial values. The average iteration time of the improved algorithm is reduced by 26.2%, the number of iterations is reduced by 69.4%, and the iteration time and the number of iterations are both small. Fluctuations within the range better solve the problem of sensitivity to the initial value. Experiments have proved that the new objective function can significantly improve the separation performance of neural networks. Compared with the existing music separation methods, the method in this paper shows excellent performance in both accompaniment and singing in separated music.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Research on Segmentation Experience of Music Signal Improved Based on Maximization of Negative Entropy

Qin

2021

Complexity

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“…Therefore, stream-level transcription is also called timbre tracking or instrument tracking in the literature. Existing works at this level are few, with [16], [10], [25] as examples.…”

Section: An Overview Of Amt Methodsmentioning

confidence: 99%

Automatic Music Transcription: An Overview

Benetos

Dixon

Duan

et al. 2019

IEEE Signal Process. Mag.

189

123

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“…They tested different timbre features for both music and speech signals. In [16], a similar method was proposed, where the authors applied Probabilistic Latent Component Analysis (PLCA) to decompose the audio signal into multi-pitch estimates and to extract source-specific features. Then, clustering was performed under the constraint of cognitive grouping of continuous pitch contours and segregation of simultaneous pitches into different source streams using Hidden Markov Random Fields.…”

Section: Related Workmentioning

confidence: 99%

Pitch-Informed Instrument Assignment Using a Deep Convolutional Network with Multiple Kernel Shapes

Lordelo,

Benetos,

Dixon

et al. 2021

Preprint

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This paper proposes a deep convolutional neural network for performing note-level instrument assignment. Given a polyphonic multi-instrumental music signal along with its ground truth or predicted notes, the objective is to assign an instrumental source for each note. This problem is addressed as a pitch-informed classification task where each note is analysed individually. We also propose to utilise several kernel shapes in the convolutional layers in order to facilitate learning of efficient timbre-discriminative feature maps. Experiments on the MusicNet dataset using 7 instrument classes show that our approach is able to achieve an average F-score of 0.904 when the original multi-pitch annotations are used as the pitch information for the system, and that it also excels if the note information is provided using third-party multi-pitch estimation algorithms. We also include ablation studies investigating the effects of the use of multiple kernel shapes and comparing different input representations for the audio and the note-related information.

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Multiple F0 Estimation and Source Clustering of Polyphonic Music Audio Using PLCA and HMRFs

Cited by 26 publications

References 28 publications

[Retracted] Research on Segmentation Experience of Music Signal Improved Based on Maximization of Negative Entropy

[Retracted] Research on Segmentation Experience of Music Signal Improved Based on Maximization of Negative Entropy

Automatic Music Transcription: An Overview

Pitch-Informed Instrument Assignment Using a Deep Convolutional Network with Multiple Kernel Shapes

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