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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27740/19:10242237

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/LM2015070" target="_blank" >LM2015070: IT4Innovations národní superpočítačové centrum</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

7

Číslo periodika v rámci svazku

Neuveden

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

75081-75089

Kód UT WoS článku

000473188800001

EID výsledku v databázi Scopus

2-s2.0-85068349969