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Effects of Soft-Masking Function on Spectrogram-Based Instrument - Vocal Separation

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Computational Linguistics (PACLING 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1215))

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Abstract

This paper presents an analysis of effects of soft-masking function on spectrogram-based instrument - vocal separation for audio signals. The function taken into consideration is of 1st-order with two masking magnitude parameters: one for background and one foreground separation. It is found that as the masking magnitude increases, the signal estimations are improved. The background signal’s spectrogram becomes closer to that of the original signal while the foreground signal’s spectrogram represents better the vocal wiggle lines compared to the original signal spectrogram. With the same increase in the masking magnitude (up to ten-fold), the effect on background signal spectrogram is more significant compared to that of foreground signal. This is evident through the significant (\(\approx \)three times) reduction of background signal’s root-mean-square (RMS) values and the less significant reduction (approximately one-third) of foreground signal’s RMS values.

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Acknowledgment

The authors would thank FPT University, Hanoi, Vietnam and UCSI University, Kuala Lumpur, Malaysia for supporting this research.

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Authors and Affiliations

  1. Computing Fundamental Department and FPT Technology Research Institute, FPT University, Hoa Lac Hi-Tech Park, Hanoi, 155300, Vietnam

    Duc Chung Tran

  2. Faculty of Engineering Technology and Built Environment, UCSI University, Kuala Lumpur, Malaysia

    M. K. A. Ahamed Khan

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  1. Duc Chung Tran
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  2. M. K. A. Ahamed Khan
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Corresponding author

Correspondence to Duc Chung Tran .

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Editors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Ishikawa, Japan

    Le-Minh Nguyen

  2. University of Engineering and Technology, Hanoi, Vietnam

    Xuan-Hieu Phan

  3. Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan

    Kôiti Hasida

  4. Japan Advanced Institute of Science and Technology, Ishikawa, Japan

    Satoshi Tojo

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Tran, D.C., Ahamed Khan, M.K.A. (2020). Effects of Soft-Masking Function on Spectrogram-Based Instrument - Vocal Separation. In: Nguyen, LM., Phan, XH., Hasida, K., Tojo, S. (eds) Computational Linguistics. PACLING 2019. Communications in Computer and Information Science, vol 1215. Springer, Singapore. https://doi.org/10.1007/978-981-15-6168-9_28

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  • DOI: https://doi.org/10.1007/978-981-15-6168-9_28

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-6167-2

  • Online ISBN: 978-981-15-6168-9

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