Designing a Neural Network Based Audio Classification System
Artificial neural networks have found profound success in the area of pattern recognition. The collections of digital music have become increasingly common over the recent years. As the amount of data increases, digital context classification is becoming more important. In this thesis, we are stud...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | eng eng |
| Published: |
2004
|
| Subjects: | |
| Online Access: | https://etd.uum.edu.my/1248/1/KHALED_ABDALGADER_MOHAMED_OMAR.pdf https://etd.uum.edu.my/1248/2/1.KHALED_ABDALGADER_MOHAMED_OMAR.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my-uum-etd.1248 |
|---|---|
| record_format |
uketd_dc |
| institution |
Universiti Utara Malaysia |
| collection |
UUM ETD |
| language |
eng eng |
| topic |
QA76 Computer software |
| spellingShingle |
QA76 Computer software Khaled Abdal Gader, Mohamed Omar Designing a Neural Network Based Audio Classification System |
| description |
Artificial neural networks have found profound success in the area of pattern recognition. The collections of digital music have become increasingly common over the recent years. As the amount of data increases, digital context classification is becoming more important. In this thesis, we are studying content-based classification of digital musical signals according to their musical genre (e.g. : jazz, rock, pop and blues) and the features uses. The purpose of this thesis is to propose of designing a neural network technique, signal processing and related works of research. In addition, the methodology that used in designing audio classification model using neural network is introduced. The method was follow in this thesis is content analysis, and the designing of the model has through several phases: requirements analysis, knowledge representation and model designing. The theory behind the used features is reviewed and the fining from the proposed designing is presented. |
| format |
Thesis |
| qualification_name |
masters |
| qualification_level |
Master's degree |
| author |
Khaled Abdal Gader, Mohamed Omar |
| author_facet |
Khaled Abdal Gader, Mohamed Omar |
| author_sort |
Khaled Abdal Gader, Mohamed Omar |
| title |
Designing a Neural Network Based Audio Classification System |
| title_short |
Designing a Neural Network Based Audio Classification System |
| title_full |
Designing a Neural Network Based Audio Classification System |
| title_fullStr |
Designing a Neural Network Based Audio Classification System |
| title_full_unstemmed |
Designing a Neural Network Based Audio Classification System |
| title_sort |
designing a neural network based audio classification system |
| granting_institution |
Universiti Utara Malaysia |
| granting_department |
Sekolah Siswazah |
| publishDate |
2004 |
| url |
https://etd.uum.edu.my/1248/1/KHALED_ABDALGADER_MOHAMED_OMAR.pdf https://etd.uum.edu.my/1248/2/1.KHALED_ABDALGADER_MOHAMED_OMAR.pdf |
| _version_ |
1747827105178058752 |
| spelling |
my-uum-etd.12482013-07-24T12:11:07Z Designing a Neural Network Based Audio Classification System 2004 Khaled Abdal Gader, Mohamed Omar Sekolah Siswazah Graduate School QA76 Computer software Artificial neural networks have found profound success in the area of pattern recognition. The collections of digital music have become increasingly common over the recent years. As the amount of data increases, digital context classification is becoming more important. In this thesis, we are studying content-based classification of digital musical signals according to their musical genre (e.g. : jazz, rock, pop and blues) and the features uses. The purpose of this thesis is to propose of designing a neural network technique, signal processing and related works of research. In addition, the methodology that used in designing audio classification model using neural network is introduced. The method was follow in this thesis is content analysis, and the designing of the model has through several phases: requirements analysis, knowledge representation and model designing. The theory behind the used features is reviewed and the fining from the proposed designing is presented. 2004 Thesis https://etd.uum.edu.my/1248/ https://etd.uum.edu.my/1248/1/KHALED_ABDALGADER_MOHAMED_OMAR.pdf application/pdf eng validuser https://etd.uum.edu.my/1248/2/1.KHALED_ABDALGADER_MOHAMED_OMAR.pdf application/pdf eng public masters masters Universiti Utara Malaysia Aksoy, M.Ayaz H & konukoglu, E (2002). Content-based classification of musical instrument sound using Gaussian methods. EE 473 DIGITAL SIGNAL PROCESSING. Aucouturier J & Pachet F (2003). Representing musical gentre: A state of the art. Journal of New Music research, vol.32, no 1, page 83-93. Battle, E & Cano P (2001). Automatic segmentation for music classification using competitive hidden markov models. Audiovisual institute. University pompeu fabra rambla 31, 08002 barcelona catalunya-spain. Becchetti C. & Ricotti P.L (1999). Speech Recognitin. Theory and C++ Implementation. John Wiley & Sons. Breure L 92001). development of the genre concept. New music journal. Retrieved April 4th 2004, from : http://www.es.uu.nl/people/leen/genredev/genredevelopment.htm. burred J & Lerch A (2003). A hierarchical approach to automatic musical genre classification. Communication system group technical university Berlin, Germany. Proc of the 6th int.conference on digital audio effect (DAFx-03), London, UK. September 8-11. Danial K (1998). Artificial neural networks. An individual project within MISB-420-0. Saint Louis University. St.Louis November. Retrieved March 22nd 2004 from http://hem.hj.se/~de96klda/neuralnetworks.htm. Erickson T. (1999). Rhyme and punishment. The creation and enforcement of conventions in an on-line participatory limerick genre. Eronen A (2003). Musical instrument recognition using ICA-based transform of features and discriminatively trained HMMs.vol 2, pages 133-136. Seventh international Symposium on Signal Processing and its application. Foote.j.a (1999). An overview of Audio Information Retrieval. Multimedia Systems, 7(1):2-10. Grimaldi.M.Cunningham.P.& Kokaram A (2001). An Evaluation of alternative feature selection strategies and ensemble technique for classifying music. Computer science department, trinity college Dublin, Ireland and electronic engineering department. Trinity College Dublin, Ireland. Hartmann. W. Pitch.P & Auditory O. (1996). Journal of the Acoustical Society of America.vol 100.no 6,pages 3491-3502. Hayin S.(1999). Neural networks: a comprehensive foundation.2nd edition. Prentice Hall. Houtsma,A.J.M. (1997). Pitch & Timbre: Definition, meaning and use. Journal of new music research.vol.26.no.2 pages 104-115.1997. Houtsma A.J.M (1995).Hearing, handbook of perception and congnition,chapter 8: pitch perception, pages 267-296. academic Press Inc. San Diego, CA, USA, 2nd edition. Hussey, J & Hussey R (1997). Business Research: A Practical guide for undergraduate and postgraduate student. 1st ed. MACMILLAN PRESS LTD. Li.D.Sethi, I. Dimitrova, N & McGee T (2001). Classification of general audio data for content-based retrieval. Pattern Recognition Letter, vol.22, no.5. pages 533-544. Liu.Z.Wang.Y & Chen. T (1998).Audio Featured Extraction and analysis for Scene. Logan B. (2000). Mel Frequency Cepstral Coefficient for music modeling. International Symposium on Music Information Retrieval. Oravec.M. (2001). Multilayer perception in Face Recognition. Faculty of Electrical engineering and Information Technology. Bratislava. Pachet F & Cazaly D (2000). Taxonomy of Musical Gentres.Paris France. Content Based Multimedia Information Access conference (RIAO). Perelmuter G Enrique V Vellasco, M & Pacheco M (2000). Recognition of Industrial Parts using Artificial Neutral Network. Department of Engineering,PUC Rio, Brazil. Pfeiffer S. Fischer S & Effelsberg W 91996). Automatic Audio Content Analysis. Proceedings of the fourth ACM international conference on Multimedia. pp.21-30. Rabiner L and Juang B.H (1993). Fundamentals of speech Recognition. PTR Prentice-Hall Inc. New Jersey. Reynolds, D. & Rose, R (1995). Robust Text-Independent Speaker Identification Using Gaussian Mixture Models. IEEE Transaction on Speech and Audio Processing,vol.3. no.1, pages 72-83. Robert,S.T (2001). digital Audio technology, Center for Audio Recording Arts (CARA). MI 313. Retrieval march 25th 2004 from: http://cara.gsu.edu/courses/MI_313/digi2.htm. Sadie S. editor (2001). The New Grove Dictionary of Music and Musicians. McMillan Publishing Company. Saunders J. (1996). Real-time Discrimination of Broadcast Speech/Music. Proc. ICASSP96.vol.II.pp 993-996.Atlanta. Scheirer E (1998). Tempo and beat analysis of acoustic musical signals. Journal of the Acoustical Society of America, vol.103.no.1 pages 558-601. Scheirer E & Slaney M (1997). Construction and Evaluation of a Robust Multifeature Speech/Music discriminator, vol.2, pages 1331-1334. IEEE International Acoustics. Speech, and Signal processing (ICASSP). Silvia. P Stephan F & Wolfgang E (1996). Content-based Classification. Search and retrieval of Audio. IEEE Multimedia, 3(3), pp.27-36. Zhang, T & Kuo C (1998). Content-based Classification and Retrieval of Audio. In SPIE's 43rd Annual Meeting-Conference on Advanced Signal Processing Algorithms. Architectures and Implementations VIII. San Diego. Zhang T & Kuo J (1999). Content-based Audio Classification and Retrieval. university of Southern California. Retrieved March 25th. 2004. From: http://viola.usc.edu/extranet/Projects/database-audio/. Zhang T & Kuo C (2001). Audio Content Analysis for Online Audio Visual Data Segmentation and Classification. IEEE Transaction on Speech and Audio Processing, vol.9,no.4 pages 441-457. |