Research on Objective Evaluation of Recording Audio Restoration Based on Deep Learning Network. (18th September 2018)
- Record Type:
- Journal Article
- Title:
- Research on Objective Evaluation of Recording Audio Restoration Based on Deep Learning Network. (18th September 2018)
- Main Title:
- Research on Objective Evaluation of Recording Audio Restoration Based on Deep Learning Network
- Authors:
- Jin, Cong
Zhao, Wei
Wang, Hongliang - Other Names:
- Li Zechao Academic Editor.
- Abstract:
- Abstract : There are serious distortion problems in the history audio and video data. In view of the characteristics of audio data repair, the intelligent technology of audio evaluation is explored. As the traditional audio subjective evaluation method requires a large number of personal to audition and evaluation, the tester's subjective sense of hearing deviation and sample space data limited the impact of the accuracy of the experiment. Based on the deep learning network, this paper designs an objective quality evaluation system for historical audio and video data and evaluates the performance of the system and the audio signal quality from the perspective of feature extraction and network parameter selection. Experiments show that the system has good performance in this experiment; the predictive results and subjective evaluation of the correlation and dispersion indicators are good, up to 0.91 and 0.19.
- Is Part Of:
- Advances in multimedia. Volume 2018(2018)
- Journal:
- Advances in multimedia
- Issue:
- Volume 2018(2018)
- Issue Display:
- Volume 2018, Issue 2018 (2018)
- Year:
- 2018
- Volume:
- 2018
- Issue:
- 2018
- Issue Sort Value:
- 2018-2018-2018-0000
- Page Start:
- Page End:
- Publication Date:
- 2018-09-18
- Subjects:
- Multimedia systems -- Periodicals
Computer networks -- Periodicals
Multimédia
Réseaux d'ordinateurs
Computer networks
Multimedia systems
Periodicals
006.7 - Journal URLs:
- https://www.hindawi.com/journals/am/ ↗
http://bibpurl.oclc.org/web/22854 ↗ - DOI:
- 10.1155/2018/3748141 ↗
- Languages:
- English
- ISSNs:
- 1687-5680
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 10572.xml