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dc.contributor.authorSabur A. Alim
dc.contributor.authorNahrul Khair Alang, Md Rashid, Dr.
dc.contributor.authorAjibola, Alim Sabur
dc.contributor.authorMd. Mozasser, Rahman
dc.date.accessioned2012-07-19T14:27:12Z
dc.date.available2012-07-19T14:27:12Z
dc.date.issued2012-02-27
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/20500
dc.descriptionInternational Conference on Man Machine Systems (ICoMMS 2012) organized by School of Mechatronic Engineering, co-organized by The Institute of Engineer, Malaysia (IEM) and Society of Engineering Education Malaysia, 27th - 28th February 2012 at Bayview Beach Resort, Penang, Malaysia.en_US
dc.description.abstractSound is one of the most important tools for classification, recognition and identification of objects in the environment. The raw sound signal is complex and is not suitable to be feed as input to the sound identification system; hence the need for a good front-end arises. The identification rate using the RNN classifier and MFCC is 72.7%, 73.7%, 78.9% 57.1% and 58.3% for aircraft, car, rain, thunder and train respectively as compared to what was obtained by using MLP. 31.6%, 19.4%, 18.5%, 38.0% and 26.4% decline is achieved for aircraft, car, rain, thunder and train respectively when comparing between MLP and RNN for MFCC. As far as sound recognition using the input used in this experiment is concerned, MFCC outperforms PLP and MFCC & PLP using MLP as classifier.en_US
dc.language.isoenen_US
dc.publisherUniversiti Malaysia Perlis (UniMAP)en_US
dc.relation.ispartofseriesProceedings of the International Conference on Man-Machine Systems (ICoMMS 2012)en_US
dc.subjectMel frequency cepstrum coefficientsen_US
dc.subjectPerceptual linear predictionen_US
dc.subjectMultilayer perceptronen_US
dc.subjectRecurrent neural networken_US
dc.titleA comparative study of the difference between MFCC and PLP in the recognition of sounden_US
dc.typeWorking Paperen_US
dc.publisher.departmentSchool of Mechatronic Engineeringen_US
dc.contributor.urlmoaj1st@yahoo.comen_US


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