In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
System Development & Application
|
App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 9
2770-2774

Multi-scale audio sequence generation method based on generative adversarial networks and feature fusion

Xu Huajiea,b,c,d
Zhang Boa
a. College of Computer & Electronic Information, b. Guangxi Key Laboratory of Multimedia Communications & Network Technology, c. Key Laboratory of Parallel, Distributed & Intelligent Computing, d. Guangxi Intelligent Digital Services Research Center of Engineering Technology, Guangxi University, Nanning 530004, China
DOI: 10.19734/j.issn.1001-3695.2023.01.0018

Abstract

Insufficient audio data scale is a common problem in the speech recognition process, and it is difficult to guarantee the effect of the speech recognition model trained with less training data. Therefore, this paper proposed a multi-scale audio sequence generation method based on generative confrontation network and feature fusion(MAS-GAN), which consisted of a multi-scale audio sequence generator and a real/fake-category discriminator. The generator learnt the features of audio sequences in different time and frequency domains through three up-sampling sub-networks, and then fused the features of different scales into pseudo audio sequence. The discriminator distinguished the generated fake data from the real data though the auxiliary classifier, and guided the generator to generate data of various categories. Experiment shows that the IS and FID scores are increased by 6.78% and 3.75% respectively compared with the current mainstream audio sequence generation methods, the proposed method can generate higher quality audio sequences; at the same time, it evaluated the quality of the generated audio sequences by performing classification experiments on the SC09 dataset, the classification accuracy is about 2.3% higher than other methods.

Foundation Support

国家自然科学基金资助项目(71963001)
广西壮族自治区科技计划资助项目(2017AB15008)
崇左市科技计划资助项目(FB2018001)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.01.0018
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 9
Section: System Development & Application
Pages: 2770-2774
Serial Number: 1001-3695(2023)09-032-2770-05

Publish History

[2023-04-03] Accepted Paper
[2023-09-05] Printed Article

Cite This Article

许华杰, 张勃. 基于生成对抗网络与特征融合的多尺度音频序列生成方法 [J]. 计算机应用研究, 2023, 40 (9): 2770-2774. (Xu Huajie, Zhang Bo. Multi-scale audio sequence generation method based on generative adversarial networks and feature fusion [J]. Application Research of Computers, 2023, 40 (9): 2770-2774. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)