Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Xianku Li"'
Publikováno v:
IEEE Access, Vol 6, Pp 58043-58055 (2018)
Surveillance systems based on image analysis can automatically detect road accidents to ensure a quick intervention by rescue teams. However, in some situations, the visual information is insufficiently reliable, whereas the use of a sound detector c
Externí odkaz:
https://doaj.org/article/eebc7f2e129f496bb2746ea6ce4b5f4b
Publikováno v:
Multimedia Tools and Applications. 78:33999-34025
In this study, we propose a method for acoustic event diarization based on a feature of deep embedding and a clustering algorithm of integer linear programming. The deep embedding learned by deep auto-encoder network is used to represent the properti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c0532c774686267bc857338645a7ac6f
https://doi.org/10.1007/s11042-019-07991-6
https://doi.org/10.1007/s11042-019-07991-6
Publikováno v:
IEEE Transactions on Information Forensics and Security. 13:965-977
Considerable attention has been paid to acquisition device recognition over the past decade in the forensic community, especially in digital image forensics. In contrast, acquisition device clustering from speech recordings is a new problem that aims
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b9502803c185d8fe81916851aca08cda
https://doi.org/10.1109/tifs.2017.2774505
https://doi.org/10.1109/tifs.2017.2774505
Publikováno v:
IEEE Access, Vol 6, Pp 58043-58055 (2018)
Surveillance systems based on image analysis can automatically detect road accidents to ensure a quick intervention by rescue teams. However, in some situations, the visual information is insufficiently reliable, whereas the use of a sound detector c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19e0fa65604bbaf0458965010fd53ad8
https://ieeexplore.ieee.org/document/8478265/
https://ieeexplore.ieee.org/document/8478265/
Publikováno v:
Digital Signal Processing. 63:123-134
An unsupervised approach based on Information Bottleneck (IB) principle is proposed for detecting acoustic events from audio streams. In this paper, the IB principle is first concisely presented, and then the practical issues related to the applicati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::37e93bb1fe12f4ba26d1fdb0f3d37d90
https://doi.org/10.1016/j.dsp.2016.12.012
https://doi.org/10.1016/j.dsp.2016.12.012
Publikováno v:
Multimedia Tools and Applications. 77:897-916
Extraction of effective audio features from acoustic events definitely influences the performance of Acoustic Event Detection (AED) system, especially in adverse audio situations. In this study, we propose a framework for extracting Deep Audio Featur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c1f9e571a12b131498eec93bb6d34c38
https://doi.org/10.1007/s11042-016-4332-z
https://doi.org/10.1007/s11042-016-4332-z
Publikováno v:
2018 International Conference on Audio, Language and Image Processing (ICALIP).
Although acoustic scene classification has been received great attention from researchers in the field of audio signal processing, it is still a challenging and unsolved task to date. In this paper, we present our work of acoustic scene classificatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f9b9e1bae1aa129432fbb225d4843c67
https://doi.org/10.1109/icalip.2018.8455765
https://doi.org/10.1109/icalip.2018.8455765
Publikováno v:
ICASSP
Acquisition device clustering from speech recordings is a new and critical problem in the field of speech forensic, which aims at merging speech recordings acquired by the same device into one cluster without both pre-knowing prior information of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cbafaa299cfddf5695c8d82964b720a6
https://doi.org/10.1109/icassp.2017.7952534
https://doi.org/10.1109/icassp.2017.7952534
Autor:
Zhuoming Chen, Xue Zhang, Aiwu Chen, Qian Huang, Xianku Li, Xiaohui Feng, Jichen Yang, Yanxiong Li
Publikováno v:
2016 International Conference on Audio, Language and Image Processing (ICALIP).
Speaker role clustering is to obtain the number of different roles and to merge the utterances of the same role into one cluster in an unsupervised way, which is important for rich transcription of multi-speaker spoken documents. This paper presents
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ed281a3576f91e2f7da92fc85ea43bee
https://doi.org/10.1109/icalip.2016.7846538
https://doi.org/10.1109/icalip.2016.7846538