dc.contributor.author |
Lalitha Narla, Venkata |
|
dc.contributor.author |
Vardhini, B.Harsha |
|
dc.contributor.author |
3S.Kavitha, N.S.S. |
|
dc.contributor.author |
.Ashritha, P |
|
dc.contributor.author |
Geetha, M. |
|
dc.date.accessioned |
2024-07-11T09:31:21Z |
|
dc.date.available |
2024-07-11T09:31:21Z |
|
dc.date.issued |
2024-07-11 |
|
dc.identifier.uri |
https://journal.uob.edu.bh:443/handle/123456789/5800 |
|
dc.description.abstract |
Accurate identification of audio source recording devices is paramount in digital forensic
investigations, including topics like copyright protection, tamper detection, and audio source forensics.
This work presented a novel method for learning feature representations using temporal audio
characteristics, such as Mel Frequency Cepstral Coefficients (MFCC) and Constant-Q Transform
(CQT), obtained from segmented acoustic features. Subsequently creates a structured representation
learning model by combining Long Short-Term Memory Networks (LSTM) with Recurrent Neural
Networks (RNN). This model efficiently condenses spatial information, resulting in accurate
recognition, by utilizing temporal modelling and time-frequency representation. The performance of
the proposed methods is tested on 10-second audio signals recorded with four different audio recording
devices. The outcomes of the experiment show an amazing degree of accuracy with 96% in classifying
four types of recording audio source devices. This method promises improved efficacy in a variety of
forensic circumstances and represents a substantial development in audio forensic analysis. The
performance metrics of audio source recording using CQT-RNN and MFCC-RNN are compared, and
also compared with state-of-the-art methods. A user interface has been developed to facilitate the
recognition of the source device for test audio signals using the proposed method. Overall, this research
marks a substantial advancement in audio forensic analysis, providing a robust, accurate, and user friendly solution for the identification of audio source recording devices, and underscoring its potential
for widespread forensic applications. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
University of Bahrain |
en_US |
dc.subject |
Digital Forensics |
en_US |
dc.subject |
, Audio Source Recording Device |
en_US |
dc.subject |
, Constant-Q Transform, |
en_US |
dc.subject |
Mel Frequency Cepstral Coefficients, |
en_US |
dc.subject |
Recurrent Neural Networks, |
en_US |
dc.subject |
Long Short-Term Memory Networks |
en_US |
dc.title |
Recognition of Audio Source Recording Device using MFCC and RNN |
en_US |
dc.identifier.doi |
XXXXXX |
|
dc.volume |
17 |
en_US |
dc.issue |
1 |
en_US |
dc.pagestart |
1 |
en_US |
dc.pageend |
18 |
en_US |
dc.contributor.authorcountry |
Surampalem, AP, India |
en_US |
dc.contributor.authoraffiliation |
Department of Electronics and Communication Engineering Aditya College of Engineering & Technology, |
en_US |
dc.source.title |
International Journal of Computing and Digital Systems |
en_US |
dc.abbreviatedsourcetitle |
IJCDS |
en_US |