A voice signal based gender prediction model using random forest classifier

This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.

Dades bibliogràfiques
Autors principals:	Ahmed, Saif, Hossain, Sajjad, Chowdhury, Gazala, Mehnaz, Maliha
Altres autors:	Uddin, Jia
Format:	Thesis
Idioma:	English
Publicat:	BRAC University 2018
Matèries:	Logistic regression Random forest Principal component analysis Classification and Regression Tree (CART)
Accés en línia:	http://hdl.handle.net/10361/10097

id	10361-10097
record_format	dspace
spelling	10361-100972022-01-26T10:21:50Z A voice signal based gender prediction model using random forest classifier Ahmed, Saif Hossain, Sajjad Chowdhury, Gazala Mehnaz, Maliha Uddin, Jia Department of Computer Science and Engineering, BRAC University Logistic regression Random forest Principal component analysis Classification and Regression Tree (CART) This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 32-33). In the proposed model, Classification and Regression Tree (CART) was used as a classifier to classify gender using four different algorithms which were tested with changing dataset frames, layer sizes and samples to get best options for our model. We had to tune our dataset with Principal Component Analyzer(PCA) which improved the accuracy rate a bit and also worked along with the algorithms. The intelligible idea of voiceprints and human-computer interaction gave us the motivation to predict gender by using different proposed classifiers that we are using in our model .Besides the overall efficiency and outcome of human-computer interaction gave us the inspiration to select this model for our thesis paper. In this existing system there are quite a lot of problem that arose while dealing with our proposed model those are over fitting of the dataset, having different layer sizes, number of decision tree and most importantly solving the hidden layer sizes. We did successfully solved most of the problems by running five different algorithms on our model which are Decision Tree Classifier, Logistic Regression, Support Vector Machine (SVM) , Multi-Layer Perceptron Classifier (MLP) and Random Forest (RF) Classifier. To use the total dataset on this algorithm we used 75% training and 25% testing of the total dataset. Due to different layers we had different accuracy result for each of the algorithms. The worst accuracy result was given by Multi-Layer Perceptron (MLP) which was 75% in two implementations and the best accuracy result was given by Random Forest Classifier which was 97.34 % from our proposed model. Saif Ahmed Sajjad Hossain Gazala Chowdhury Maliha Mehnaz B. Computer Science and Engineering 2018-05-09T05:04:31Z 2018-05-09T05:04:31Z 2018 2018-04 Thesis ID 12201021 ID 13101252 ID 13201036 ID 13301015 http://hdl.handle.net/10361/10097 en BRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 33 pages application/pdf BRAC University
institution	Brac University
collection	Institutional Repository
language	English
topic	Logistic regression Random forest Principal component analysis Classification and Regression Tree (CART)
spellingShingle	Logistic regression Random forest Principal component analysis Classification and Regression Tree (CART) Ahmed, Saif Hossain, Sajjad Chowdhury, Gazala Mehnaz, Maliha A voice signal based gender prediction model using random forest classifier
description	This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
author2	Uddin, Jia
author_facet	Uddin, Jia Ahmed, Saif Hossain, Sajjad Chowdhury, Gazala Mehnaz, Maliha
format	Thesis
author	Ahmed, Saif Hossain, Sajjad Chowdhury, Gazala Mehnaz, Maliha
author_sort	Ahmed, Saif
title	A voice signal based gender prediction model using random forest classifier
title_short	A voice signal based gender prediction model using random forest classifier
title_full	A voice signal based gender prediction model using random forest classifier
title_fullStr	A voice signal based gender prediction model using random forest classifier
title_full_unstemmed	A voice signal based gender prediction model using random forest classifier
title_sort	voice signal based gender prediction model using random forest classifier
publisher	BRAC University
publishDate	2018
url	http://hdl.handle.net/10361/10097
work_keys_str_mv	AT ahmedsaif avoicesignalbasedgenderpredictionmodelusingrandomforestclassifier AT hossainsajjad avoicesignalbasedgenderpredictionmodelusingrandomforestclassifier AT chowdhurygazala avoicesignalbasedgenderpredictionmodelusingrandomforestclassifier AT mehnazmaliha avoicesignalbasedgenderpredictionmodelusingrandomforestclassifier AT ahmedsaif voicesignalbasedgenderpredictionmodelusingrandomforestclassifier AT hossainsajjad voicesignalbasedgenderpredictionmodelusingrandomforestclassifier AT chowdhurygazala voicesignalbasedgenderpredictionmodelusingrandomforestclassifier AT mehnazmaliha voicesignalbasedgenderpredictionmodelusingrandomforestclassifier
_version_	1814309483232362496

A voice signal based gender prediction model using random forest classifier

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