Information Redundancy in Constructing Systems for Audio Signal Examination on Deep Learning Neural Networks

被引：0

作者：

V. I. Solovyov

O. V. Rybalskiy

V. V. Zhuravel

A. N. Shablya

E. V. Tymko

机构：

[1] Silentium Systems,

[2] National Academy of Internal Affairs,undefined

[3] Kyiv Expert and Forensic Center of the Ministry of Internal Affairs of Ukraine,undefined

[4] Odessa Scientific Research Institute of Forensic Expertise of the Ministry of Justice of Ukraine,undefined

[5] Kyiv Scientific Research Institute of Forensic Expertise of the Ministry of Justice of Ukraine,undefined

来源：

Cybernetics and Systems Analysis | 2022年 / 58卷

关键词：

Morlet wavelet; time window; time-frequency transformation; speaker; identification; redundancy; neural network; spectrum; phonogram;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Preliminary signal processing methods used to create new tools to examine materials and digital sound recording means are described. It is shown that using information redundancy when creating a training base for deep learning neural networks used for such examination increases speaker identification efficiency based on voice characteristic parameters by about 15%. It is shown that the proposed processing methods enable speaker identification based on phonograms that are 1 second long.

引用

页码：8 / 15

页数：7

共 50 条

[1] Information Redundancy in Constructing Systems for Audio Signal Examination on Deep Learning Neural Networks
Solovyov, V., I
Rybalskiy, O., V
Zhuravel, V. V.
Shablya, A. N.
Tymko, E., V
CYBERNETICS AND SYSTEMS ANALYSIS, 2022, 58 (01) : 8 - 15
[2] Deep Neural Networks for Shimmer Approximation in Synthesized Audio Signal
Alejandro Garcia, Mario
Atilio Destefanis, Eduardo
COMPUTER SCIENCE (CACIC 2017), 2018, 790 : 3 - 12
[3] Constructing Deep Neural Networks by Bayesian Network Structure Learning
Rohekar, Raanan Y.
Nisimov, Shami
Gurwicz, Yaniv
Koren, Guy
Novik, Gal
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), 2018, 31
[4] Audio signal processing by neural networks
Uncini, A
NEUROCOMPUTING, 2003, 55 (3-4) : 593 - 625
[5] Feature Learning and Signal Propagation in Deep Neural Networks
Lou, Yizhang
Mingard, Chris
Hayou, Soufiane
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162, 2022,
[6] Deep Learning for Audio Signal Processing
Purwins, Hendrik
Li, Bo
Virtanen, Tuomas
Schlueter, Jan
Chang, Shuo-Yiin
Sainath, Tara
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2019, 13 (02) : 206 - 219
[7] A COMPARISON OF AUDIO SIGNAL PREPROCESSING METHODS FOR DEEP NEURAL NETWORKS ON MUSIC TAGGING
Choi, Keunwoo
Fazekas, Gyorgy
Sandler, Mark
Cho, Kyunghyun
2018 26TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2018, : 1870 - 1874
[8] auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks
Freitag, Michael
Amiriparian, Shahin
Pugachevskiy, Sergey
Cummins, Nicholas
Schuller, Bjoern
JOURNAL OF MACHINE LEARNING RESEARCH, 2018, 18
[9] Between Homomorphic Signal Processing and Deep Neural Networks: Constructing Deep Algorithms for Polyphonic Music Transcription
Su, Li
2017 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC 2017), 2017, : 884 - 891
[10] Deep learning neural networks: Methods, systems, and applications
Wei, Qinglai
Kasabov, Nikola
Polycarpou, Marios
Zeng, Zhigang
NEUROCOMPUTING, 2020, 396 : 130 - 132

← 1 2 3 4 5 →