Sinaptik Etkinlik Fonksiyon Tabanlı Sızdıran Entegre ve Ateşleme Nöron Modelini Kullanarak İnsan Ses Sinyallerinde Cinsiyet Tespiti

Abstract

Günümüzdeki teknolojik gelişmeler, insanların bir ses sinyalinden konuşmacının cinsiyetini belirlemesi mümkün kılmıştır. Frekans türleri, spektral ve entropi gibi sayısal nitelikli veriler ses sinyallerinin akustik bilgilerini oluşturmaktadır. Son zamanlarda, yüksek başarı oranlarına sahip yapay zekâ tabanlı öğrenme modelleri çeşitli alanlarda ilgi görmeye başladı. Ses sinyalleri üzerinde derin öğrenme modelleri ile ilgili birçok çalışma bulunmaktadır. Bu çalışmada, derin öğrenme modellerinden esinlenerek tasarlanmış ve farklı bir mimari yapısı olan ani sivri uçlu sinir ağları kullanılmıştır. Çalışmada kullanılan veri kümesi, insan konuşmalarını ve seslerini içeren akustik bilgiye dayalı parametrelerden oluşmaktadır. Belirlenen veri seti kullanılarak ani sivri uçlu sinir ağı modeli eğitilmiş ve cinsiyet tespitinin gerçekleştirilmesi sağlanmıştır. Önermiş olduğumuz bu çalışmada sonuç olarak, sınıflandırma sürecinde %98,84 genel doğruluk başarısı elde edilmiştir. Bu çalışmada gerçekleştirilen deneysel analizler ile ani sivri uçlu sinir ağı modelinin başarılı bir şekilde çalıştırıldığı, yüksek başarımlar elde edildiği gözlemlenmiştir.

Keywords

• Ani sivri uçlu ağlar
• Cinsiyet tespiti
• Derin öğrenme
• Sinyal işleme.

Gender Determination in Human Voice Signals using Synaptic Efficacy Function-based Leaky Integrate and Fire Neuron Model

Abstract

Today's technological advances have made it possible for people to determine the gender of the speaker from an audio signal. Numerical data such as frequency types, spectral and entropy constitute acoustic information of audio signals. Recently, artificial intelligence-based learning models with high success rates have started to attract attention in various fields. There are many studies on deep learning models on audio signals. In this study, spiked neural networks with a different architectural structure, inspired by deep learning models, were used. The dataset used in the study consists of parameters based on acoustic information including human speech and voices. By using the determined data set, the spiked neural network model was trained and gender determination was achieved. As a result, 98.84% overall accuracy success was achieved in the classification process in this proposed study. With the experimental analyzes carried out in this study, it was observed that the spiked neural network model was successfully run and high performances were obtained.

Keywords

• Spiking network
• Gender detection
• Deep learning
• Signal processing

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