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DeepVoCoder: A CNN Model for Compression and Coding of Narrow Band Speech

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10242237" target="_blank" >RIV/61989100:27240/19:10242237 - isvavai.cz</a>

  • Alternative codes found

    RIV/61989100:27740/19:10242237

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/8730308" target="_blank" >https://ieeexplore.ieee.org/document/8730308</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ACCESS.2019.2920663" target="_blank" >10.1109/ACCESS.2019.2920663</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    DeepVoCoder: A CNN Model for Compression and Coding of Narrow Band Speech

  • Original language description

    This paper proposes a convolutional neural network (CNN)-based encoder model to compress and code speech signal directly from raw input speech. Although the model can synthesize wideband speech by implicit bandwidth extension, narrowband is preferred for IP telephony and telecommunications purposes. The model takes time domain speech samples as inputs and encodes them using a cascade of convolutional filters in multiple layers, where pooling is applied after some layers to downsample the encoded speech by half. The final bottleneck layer of the CNN encoder provides an abstract and compact representation of the speech signal. In this paper, it is demonstrated that this compact representation is sufficient to reconstruct the original speech signal in high quality using the CNN decoder. This paper also discusses the theoretical background of why and how CNN may be used for end-to-end speech compression and coding.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20203 - Telecommunications

Result continuities

  • Project

    <a href="/en/project/LM2015070" target="_blank" >LM2015070: IT4Innovations National Supercomputing Center</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2019

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    IEEE Access

  • ISSN

    2169-3536

  • e-ISSN

  • Volume of the periodical

    7

  • Issue of the periodical within the volume

    Neuveden

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    9

  • Pages from-to

    75081-75089

  • UT code for WoS article

    000473188800001

  • EID of the result in the Scopus database

    2-s2.0-85068349969