Ses verilerinden cinsiyet tespiti için yeni bir yaklaşım: Optimizasyon yöntemleri ile özellik seçimi

Year 2023, Volume: 38 Issue: 2, 1179 - 1192, 07.10.2022

Feyza Altunbey Özbay , Erdal Özbay

https://doi.org/10.17341/gazimmfd.938294

Cited By: 2

Abstract

Son yıllarda, birçok farklı uygulama alanına sahip cinsiyet tespiti, konuşma analizinin önemli bir problemidir. Cinsiyet tespiti için perde, medyan, frekans gibi ses verilerinin farklı özelliklerinden yararlanılmaktadır. Bu çalışmada, ses verilerinden cinsiyet tespiti için metasezgisel optimizasyon algoritmalarını temel alan özellik seçimi yöntemi önerilmiştir. Önerilen yöntemde, ses verilerini en uygun biçimde temsil edecek özellik kümesi optimizasyon algoritmaları ile seçilmiş ve elde edilen özellikler kullanılarak yapay zekâ algoritmaları ile cinsiyet tespiti yapılmıştır. Ses verilerinden özellik seçimi yapmak için karmaşık problemleri çözmek konusunda yeteneklere sahip doğadan esinlenmiş metasezgisel optimizasyon algoritmaları kullanılmıştır. Parçacık Sürüsü Optimizasyonu (PSO), Karınca Koloni Optimizayonu (KKO), Salp Sürüsü Algoritması (SSA) ve Balina Optimizasyonu Algoritması (BOA) ses verilerinden özellik seçimi için ilk kez modellenmiştir. Metasezgisel optimizasyon algoritmalarının etkinliğini ölçmek için genel erişime açık veri kümesi kullanılmıştır. PSO, KKO, SSA ve BOA’nın özellik seçimi için performansları uygunluk fonksiyonu değeri, doğruluk değeri ve seçilen özellik sayısı olmak üzere üç farklı ölçüt bakımından karşılaştırılmıştır. Metasezgisel optimizasyon algoritmaları ile özellik seçimi yapıldıktan sonra elde edilen yeni veri kümeleri ve orijinal veri kümesine Naive Bayes ve Karar Ağacı algoritmaları uygulanmıştır. Yapılan analizler sonucunda, metasezgisel optimizasyon algoritmalarını özellik seçimi için kullanan bu yöntem sayesinde Naive Bayes ve Karar Ağacı algoritmaları ile elde edilen sonuçlarda başarı oranın arttığı gözlemlenmiştir.

Keywords

Cinsiyet tespiti, özellik seçimi, optimizasyon, sınıflandırma

References

• Gamit M.R., Dhameliya K., Bhatt N.S., Classification techniques for speech recognition: a review, International Journal of Emerging Technology and Advanced Engineering, 5 (2), 58-63, 2015.
• Zhong N., Dong J., Ohsuga S., Using rough sets with heuristics for feature selection, Journal of intelligent information systems, 16 (3), 199-214, 2001.
• Guyon I., Elisseeff A., An introduction to variable and feature selection, Journal of machine learning research, 3 (Mar), 1157-1182, 2003.
• Chen C.H., A hybrid intelligent model of analyzing clinical breast cancer data using clustering techniques with feature selection, Applied Soft Computing, 20, 4-14, 2014.
• Rui Y., Huang T.S., Chang S.F., Image retrieval: Current techniques, promising directions, and open issues, Journal of visual communication and image representation, 10 (1), 39-62, 1999.
• Yang Y., Pedersen J.O., A comparative study on feature selection in text categorization, In: Proceedings of the fourteenth international conference on machine learning, 412-420, Temmuz, 1997.
• Ng K., Liu H., Customer retention via data mining, Artificial Intelligence Review, 14 (6), 569-590, 2000.
• Ben-Dor A., Bruhn L., Friedman N., Nachman I., Schummer M., Yakhini Z., Tissue classification with gene expression profiles, In Proceedings of the fourth annual international conference on Computational molecular biology, 54-64, Nisan, 2000.
• Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., Lander E.S., Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science, 286 (5439), 531-537, 1999.
• Yu Z., Li L., Gao Y., You J., Liu J., Wong H.S., Han G. Hybrid clustering solution selection strategy, Pattern Recognition, 47 (10), 3362-3375, 2014.
• Rizk-Allah R.M., El-Sehiemy R.A., Deb S., Wang G.G., A novel fruit fly framework for multi-objective shape design of tubular linear synchronous motor, The Journal of Supercomputing, 73 (3), 1235-1256, 2017.
• Yang X.S., Firefly algorithm, stochastic test functions and design optimisation, International journal of bio-inspired computation, 2 (2), 78-84, 2010.
• Arora S., Singh S., Node localization in wireless sensor networks using butterfly optimization algorithm, Arabian Journal for Science and Engineering, 42 (8), 3325-3335, 2017.
• Yi J.H., Wang J., Wang G.G., Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem, Advances in Mechanical Engineering, 8 (1), 1687814015624832, 2016.
• Rizk-Allah R.M., El-Sehiemy R.A., Wang G.G., A novel parallel hurricane optimization algorithm for secure emission/economic load dispatch solution, Applied Soft Computing, 63, 206-222, 2018.
• Wang G.G., Chu H.E., Mirjalili S., Three-dimensional path planning for UCAV using an improved bat algorithm, Aerospace Science and Technology, 49, 231-238, 2016.
• Wu G., Pedrycz W., Li H., Ma M., Liu J., Coordinated planning of heterogeneous earth observation resources, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46 (1), 109-125, 2015.
• Zhang J.W., Wang G.G., Image matching using a bat algorithm with mutation, In Applied Mechanics and Materials, 203, 88-93, 2012.
• Feng Y.H., Wang G.G., Binary moth search algorithm for discounted {0-1} knapsack problem, IEEE Access, 6, 10708-10719, 2018.
• Jensen R., Shen Q., Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches, IEEE Transactions on knowledge and data engineering, 16 (12), 1457-1471, 2004.
• Hedar A.R., Wang J., Fukushima M., Tabu search for attribute reduction in rough set theory, Soft Computing, 12 (9), 909-918, 2008.
• Bello R., Gomez Y., Nowe A., Garcia M.M., Two-step particle swarm optimization to solve the feature selection problem, In Seventh International Conference on Intelligent Systems Design and Applications, 691-696, Ekim, 2007.
• Wang J., Li T., Ren R., A real time IDSs based on artificial bee colony-support vector machine algorithm, In Third International Workshop on Advanced Computational Intelligence, 91-96, Ağustos, 2010.
• Kabir M.M., Shahjahan M., Murase K., A new local search based hybrid genetic algorithm for feature selection, Neurocomputing, 74 (17), 2914-2928, 2011.
• Maka T., Dziurzanski P., An analysis of the influence of acoustical adverse conditions on speaker gender identification, In XXII Annual Pacific Voice Conference, Krakow, Polonya, 1-4, Nisan, 2014.
• Bisio I., Lavagetto F., Marchese M., Sciarrone A., Frà C., Valla M., Spectra: A speech processing platform as smartphone application, IEEE international conference on communications, Londra, UK, 7030-7035, 8-12 Haziran, 2015.
• Pahwa A., Aggarwal G., Speech feature extraction for gender recognition, International Journal of Image, Graphics and Signal Processing, 8 (9), 17, 2016.
• Přibil J., Přibilová A., Matoušek J., GMM-based speaker gender and age classification after voice conversion, First International Workshop on Sensing, Processing and Learning for Intelligent Machines, Aalborg, Danimarka, 1-5, 6-8 Temmuz, 2016.
• Buyukyilmaz M., Cibikdiken A.O., Voice gender recognition using deep learning, International Conference on Modeling, Simulation and Optimization Technologies and Applications, 409-411, Aralık, 2016.
• Barkana B.D., Zhou J., A new pitch-range based feature set for a speaker’s age and gender classification, Applied Acoustics, 98, 52-61, 2015.
• Ramdinmawii E., Mittal V.K., Gender identification from speech signal by examining the speech production characteristics, International Conference on Signal Processing and Communication, 244-249, Aralık, 2016.
• Hebbar R., Somandepalli K., Narayanan S.S., Improving Gender Identification in Movie Audio Using Cross-Domain Data, In INTERSPEECH, 282-286, 2018.
• Kabil S.H., Muckenhirn H., Magimai-Doss M., On Learning to Identify Genders from Raw Speech Signal Using CNNs, In INTERSPEECH, 287-291, 2018.
• Kaggle. Gender Recognition by Voice. https://www.kaggle.com/primaryobjects/voicegender. Erişim tarihi Nisan 21, 2021.
• Duda R.O. ve Hart P.E., Pattern classification and scene analysis, 3, 731-739, Wiley, New York, 1973.
• Mladenić D., Feature selection for dimensionality reduction, In International Statistical and Optimization Perspectives Workshop” Subspace, Latent Structure and Feature Selection”, Berlin, Almanya, 84-102, Şubat, 2005.
• Eberhart R., Kennedy J., A new optimizer using particle swarm theory, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 39-43, Ekim, 1995.
• Dorigo M., Birattari M., Stutzle T., Ant colony optimization, IEEE computational intelligence magazine, 1 (4), 28-39, 2006.
• Mirjalili S., Gandomi A.H., Mirjalili S.Z., Saremi S., Faris H., Mirjalili S.M., Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems, Advances in Engineering Software, 114, 163-191, 2017.
• Henschke N., Everett J.D., Richardson A.J., Suthers I.M., Rethinking the role of salps in the ocean, Trends in Ecology & Evolution, 31 (9), 720-733, 2016.
• Hof P.R., Van der Gucht E., Structure of the cerebral cortex of the humpback whale, Megaptera novaeangliae (Cetacea, Mysticeti, Balaenopteridae), The Anatomical Record: Advances in Integrative Anatomy and Evolutionary Biology: Advances in Integrative Anatomy and Evolutionary Biology, 290 (1), 1-31, 2007.
• Mirjalili S. ve Lewis A., The whale optimization algorithm, Advances in engineering software, 95, 51-67, 2016.