The week's pick
Reseach Article
A Robust Environmental Sound Recognition System using Frequency Domain Features
by
T. Sivaprakasam,
P. Dhanalakshmi
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 80 - Number 9 |
| Year of Publication: 2013 |
| Authors: T. Sivaprakasam, P. Dhanalakshmi |
10.5120/13887-1800
|
T. Sivaprakasam, P. Dhanalakshmi . A Robust Environmental Sound Recognition System using Frequency Domain Features. International Journal of Computer Applications. 80, 9 ( October 2013), 5-10. DOI=10.5120/13887-1800
@article{
10.5120/13887-1800,
author = {
T. Sivaprakasam,
P. Dhanalakshmi
},
title = { A Robust Environmental Sound Recognition System using Frequency Domain Features },
journal = {
International Journal of Computer Applications
},
issue_date = { October 2013 },
volume = { 80 },
number = { 9 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = {
5-10
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume80/number9/13887-1800/
},
doi = { 10.5120/13887-1800 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:54:05.003103+05:30
%A T. Sivaprakasam
%A P. Dhanalakshmi
%T A Robust Environmental Sound Recognition System using Frequency Domain Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 80
%N 9
%P 5-10
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract
In ubiquitous environments, analysis and classification of sound plays a critical role in various acoustic-based recognition systems. This work aims to contribute towards building an automatic sound recognition system that can understand the surrounding environment by the audio information. In this paper, an acoustic signal based context awareness system is proposed for detecting sound events in five different real-world environment. This approach is based on Back Propagation Neural Network (BPNN) classifier using a new feature set from frequency-domain features. The experiments on various categories illustrate that the results of recognition are significant and effective.
References
- Selina Chu, Shrikanth Narayanan and C. -C. Jay Kuo, "Environmental Sound Recognition With Time-Frequency Audio Features", IEEE Transactions on Audio, Speech, and Language Processing, Vol. 17, No. 17, NO. 6, AUGUST 2009.
- Behnaz Ghoraani, and Sridhar Krishnan, "Time–Frequency Matrix Feature Extraction and Classification of Environmental Audio Signals", IEEE Transactions on Audio, Speech, and Language Processing, Vol. 19, No. 7, September 2011.
- Jia-Ching Wang, Hsiao-Ping Lee, Jhing-Fa Wang, and Cai-Bei Lin, "Robust Environmental Sound Recognition for Home Automation", IEEE Transactions on Automation Science and Engineering, Vol. 5, No. 1, January 2008.
- Asma Rabaoui, Manuel Davy, Stéphane Rossignol, and Noureddine Ellouze, "Using One-Class SVMs and Wavelets for Audio Surveillance", IEEE Transactions on Information Forensics and Security, Vol. 3, No. 4, December 2008.
- Regunathan Radhakrishnan, Ajay Divakaran and Paris Smaragdis, "Audio Analysis for Surveillance Applications", 2005 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.
- Supreet Kaur, and Er. Ranjeet Kaur Sandhu, "A Survey on Enhanced Human Identification Using Gait Recognition based on Neural Network And Support Vector Machine", International Journal of Application or Innovation in Engineering & Management (IJAIEM), Web Site: www. ijaiem. org Email: editor@ijaiem. org, editorijaiem@gmail. com, Vol. 2, Issue 6, June 2013 ISSN 2319 – 4847 .
- Saurabh Karsoliya , "Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture", International Journal of Engineering Trends and Technology Volume3, Issue 6- 2012, ISSN: 2231-5381.
- Ling Ma, Dan Smith and Ben Milner , "Environmental Noise Classification for Context-Aware Applications", School of Computing Sciences, University of East Anglia Norwich, NR4 7TJ, UK, {ling. ma, dan. smith, b. milner}@uea. ac. uk .
- Michael Cowling and Renate Sitte, "Analysis of Speech Recognition Techniques for use in a Non-Speech Sound Recognition System", Griffith University Faculty of Engineering & Information Technology, Gold Coast, Qld, Australia 9726.
- Zixing Zhang and Bj¨orn Schuller, "Semi-Supervised Learning Helps in Sound Event Classification", Institute for Human-Machine Communication, Technische Universit¨at M¨unchen, Germany, zixing. zhang|schuller)@tum. de .
- Victoria Felker, Mustaque Hossain, Yacoub Najjar , Richard Barezinsky , "Modeling the Roughness of Kansas PCC Pavements: A Dynamic ANN Approach", For Presentation at the 82nd Annual Meeting of the Transportation Research Board January 12-16, 2003 and Publication in the Transportation Research Record.
- Selina Chu, "Unstructured Audio Classification for Environment Recognition", Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008).
- Dr. Yadana Thein , San Su Su Yee, "High Accuracy Myanmar Handwritten Character Recognition using Hybrid approach through MICR and Neural Network" , IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 6, November 2010 ISSN (Online): 1694-0814.
- David Gerhard , "Audio Signal Classification: History and Current Techniques", Technical Report TR-CS 2003-07 November, 2003, ISSN 0828-3494, ISBN 0 7731 0456 9.
- Dalibor Mitrovi_, Matthias Zeppelzauer, Horst Eidenberger, "On Feature Selection in Environmental Sound Recognition", 51st International Symposium ELMAR-2009, 28-30 September 2009, Zadar, Croatia.
- Jian-Da Wu, Yi-Jang Tsai, "Speaker identification system using empirical mode decomposition and an artificial neural network", Expert Systems with Applications 38 (2011) 6112–6117.
- T. B. Adam, Md Salam, "Spoken English Alphabet Recognition with Mel Frequency Cepstral Coefficients and Back Propagation Neural Networks", International Journal of Computer Applications (0975 – 8887) Vol. 42– No. 12, March 2012.
- Giovanni De Poli, "From audio to content", Chapter 4, Copyright 2006.
- Wei Chu, "Auditory-Based Noise-Robust Audio Classification Algorithms", Dept. of Electrical & Computer Engineering, McGill University Montreal, Canada, September 2008, thesis submitted to McGill University for fulfillment for the degree of Doctor of Philosophy.
- Eric Allamanche, J¨urgen, Oliver Hellmuth, Thorsten Kastner, Christian Ertel, "A Multiple Feature Model for Musical Similarity Retrieval" , Fraunhofer Institute Integrierte Schaltungen, IIS Am Wolfsmantel 33 D-91058 Erlangen, Germany.
- Renato Eduardo Silva Panda, "Automatic Mood Tracking in Audio Music", Master in Informatics Engineering, M. Sc. Thesis, panda@student. dei. uc. pt Thesis Supervisor: Professor Rui Pedro Paiva July, 2010.
- Federico Avanzini Riccardo Levorato Emanuele Menegatti, "GMM Classification of Environmental Sounds for Surveillance Applications", University of Padova Department of Information Engineering, Master's Degree in Computer Engineering (Laurea Magistrale in Ingegneria Informatica) Graduation Date: Padova, October 26th, 2010.
- Kyuwoong Hwang and Soo-Young Lee, "Environmental Audio Scene and Activity Recognition through Mobile-Based Crowdsourcing", IEEE Transactions on Consumer Electronics, Vol. 58, No. 2, May 2012.
- Woo-Hyun Choi, Seung-Il Kim, Min-Seok Keum and David K. Han, Hanseok Ko, "Acoustic and Visual Signal Based Context Awareness System for Mobile Application", IEEE Transactions on Consumer Electronics, Vol. 57, No. 2, May 2011.
- J. D. Krijnders, M. E. Niessen, T. C. Andringa, "Sound event recognition through expectancy-based evaluation of signal-driven hypotheses", ELSEVIER, Pattern Recognition Letters 31 (2010) 1552–1559.
- Laura Enflo, "Spectral Tilt Used for Automatic Detection of Prominence: A Comparison between Electroglottography and Audio", (Term Paper for Machine Learning, a GSLT course 2009).
Index Terms
Computer Science
Information Sciences
Keywords