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  • Proceedings of the 7th Conference on Sound and Music Technology (CSMT): Revised Selected Papers

    Proceedings of the 7th Conference on Sound and Music Technology (CSMT) by Li, Haifeng; Li, Shengchen; Ma, Lin;

    Revised Selected Papers

    Series: Lecture Notes in Electrical Engineering; 635;

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      • Publisher's listprice EUR 160.49
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        66 563 Ft (63 393 Ft + 5% VAT)
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      • Discounted price 53 250 Ft (50 714 Ft + 5% VAT)

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    Product details:

    • Edition number 1st ed. 2020
    • Publisher Springer Nature Singapore
    • Date of Publication 22 December 2020
    • Number of Volumes 1 pieces, Book

    • ISBN 9789811527586
    • Binding Paperback
    • See also 9789811527555
    • No. of pages143 pages
    • Size 235x155 mm
    • Weight 454 g
    • Language English
    • Illustrations VIII, 143 p. 1 illus. Illustrations, black & white
    • 122

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    Long description:

    The book presents selected papers that have been accepted at the seventh Conference on Sound and Music Technology (CSMT) in December 2019, held in Harbin, Hei Long Jiang, China. CSMT is a domestic conference focusing on audio processing and understanding with bias on music and acoustic signals. The primary aim of the conference is to promote the collaboration between art society and technical society in China. The organisers of CSMT hope the conference can serve as a platform for interdisciplinary research. In this proceeding, the paper included covers a wide range topic from speech, signal processing and music understanding, which demonstrates the target of CSMT merging arts and science research together.

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    Table of Contents:

    Bandwidth Extension WaveNet for Bone-Conducted Speech Enhancement.- Naturalness evaluation of synthetic speech based on residual learning networks.- Detection of Operation Type and Order for Digital Speech.- Singing Voice Detection Using Multi-Feature Deep Fusion with CNN.- A Multi-task Learning Approach for MelodyExtraction.- A post-processing of onset detection based on verification with neural network.- Transfer learning for music classification and regression tasks using artist tags.

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