Coşkucan BÜYÜKYILDIZ, Ismail SARITAS, Ali YAŞAR

Ses Analiziyle Duyguların Sınıflandırılması

Sınıflandırma, veri örneklerini ayırt edebilmek için kullanılan önemli bir tekniktir. Bu çalışmada öz nitelikler çıkartılarak, duygulara göre sesin sınıflandırılması amaçlanmıştır. Neşeli, sinirli, nötr ve uykulu olmak üzere dört farklı duyguda konuşan iki erkek ve iki kadın bireyden alınan ses verileri kullanılmıştır. Sesin özniteliklerinde; Kepstral özellik olarak “Mel-Frekansı Kepstral Katsayıları”, Spektral Özellik olarak “Ağırlık Merkezi, Pürüzsüzlük, Çarpıklık, Tepe, Akış, Eğim, Azalma, Basıklık, Yayılma, Entropi, Yuvarlanma noktası”, Periyodisite Özelliği olarak “Ses perdesi, Harmonik oran” kullandık. Daha sonra, Matlab’da bulunan “sınıflandırma öğrenici” araç kutusunda yer alan tüm sınıflandırma algoritmalarını veriye uyguladık ve en yüksek doğruluğu sağlayan algoritmayla duyguyu tahmin etmeye çalıştık. Sınıflandırma çalışmasında yer alan her bir veri, yirmi altı öz nitelik girdisi ve bir etiketli çıktı değerine sahiptir. Performans sonuçlarına göre, destek vektör makine algoritması en yüksek doğruluk değerini sağlamıştır. Elde edilen performans çıktıları göz önüne alındığında, bu çalışma, duyusal veriler ve ses öznitelikleri kullanılarak sesleri ayırt etmenin ve sınıflandırmanın mümkün olduğunu ortaya koymaktadır.

Anahtar Kelimeler:

Destek Vektör Makinesi, Duygu Tanımlama, Makine Öğrenmesi Veritabanı, Ses Öznitelikleri, Sınıflandırma

Classification of Emotion with Audio Analysis

Classification is an important technique used to distinguish data samples. The aim of this study is to classify according to emotions by extracting audio features. Two male and two female individuals expressed four different emotions as "fun", "angry", "neutral" and "sleepy" in the voice data. We used to “MFCC” as a Cepstral feature, “Centroid, Flatness, Skewness, Crest, Flux, Slope, Decrease, Kurtosis, Spread, Entropy, roll off point” as Spectral Feature, “Pitch, Harmonic ratio” as Periodicity Features in the sound features. After, we applied to the data that all the classification algorithms located in the classification learner toolbox in Matlab and we tried to classify the emotion with the algorithm that provides the highest accuracy. Each data in the classification study has twenty-six features inputs and one labeled output value. According to the results, support vector machine algorithm provided the highest accuracy performance. Considering the performances obtained, this study reveals that it is possible to distinguish and classify sounds using sentimental data and sound feature parameters.

Keywords:

Audio Features, Classification, Emotion Identifier, Machine Learning Database, Support Vector Machine,

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