RPCA-based real-time speech and music separation method

Mirbeygi, Mohaddeseh; Mahabadi, Aminollah; Ranjbar, Akbar

doi:10.1016/j.specom.2020.12.003

Cited by 8 publications

(2 citation statements)

References 37 publications

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“…Based on 8 hours of training with 120 inputs, the analysis is performed for the metrics assistance ratio, fusion rate, error, and processing time. In the comparative analysis, the methods RPCA [23], MORec [22],…”

Section: Resultsmentioning

confidence: 99%

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Application of Information Teaching in Vocal Music Assisted Practice in Mobile Internet Era

Liu¹,

Wang²

2022

Preprint

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Vocal music practices using the internet and mobile computing rely on the distinguishable pitch, note, and musical library information for ease of understanding. For addressing this problem an Assisted Data Processing Model for Tutor Function (ADPM-TF) is projected in this article. The proposed model learns the user requirements and their improvements based on training hours. The learned inputs are validated for the mishandled tutor sessions for identifying errors. The tutor assistance based on independent and joint (fused) data is recommended by the neural network learning based on previous session user performance/ rating. This process is streamlined depending on the user preference and switching ability over various sessions. The performance is validated using the metrics assistance ratio, error, processing time, and fusion rate.

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Section: Resultsmentioning

confidence: 99%

“…Mirbeygi et al [23] proposed a robust principal component analysis (RPCA) based music separation method. A random singular value decomposition algorithm is used here to identify the key values of the optimization process that produce a feasible set of data.…”

Section: Related Workmentioning

confidence: 99%