MFCC Öznitelikleri ve Adaboost Topluluk Öğrenme Yöntemi Kullanılarak Uyku Seslerinin Sınıflandırılması

Düzenli ve kaliteli bir gece uykusu insan hayatında hayati önem taşımaktadır. Uyku kalitesi, insanların ve çevrelerindekilerin günlük yaşamları üzerinde büyük bir etkiye sahiptir. Günümüzde birçok insan uyku bozuklukları konusunda sıkıntı çekmektedir. Bu tarz rahatsızlıklar günlük hayatı etkilemekte ve akıl sağlığını bozabilmektedir. Bu çalışma uyku seslerinin otomatik olarak sınıflandırılması için topluluk öğrenme yöntemini kullanan bir yaklaşım önermektedir. Çalışmada 7 farklı uyku sesini içeren bir veri kümesinden faydalanılmıştır. Öncelikli olarak ses dosyalarından MFCC öznitelikleri çıkartılmıştır. Sonrasında çıkartılan öznitelikler ses sınıflandırılmasında sıklıkla kullanılan lojistik regresyon, destek vektör makinesi, kNN ve rastgele orman gibi bilinen yöntemlerle sınıflandırılmıştır. Sınıflandırma başarısını artırmak amacı ile bu temel sınıflandırıcılar Adaboost topluluk öğrenme yöntemi ile birlikte kullanılması yaklaşımı önerilmiştir. Önerilen yaklaşım ile sınıflandırma başarısında artış gözlemlenmiştir. En başarılı sonuç %96.439 ile Adaboost+Rastgele orman yönteminden elde edilmiştir.

Anahtar Kelimeler:

Adaboost, topluluk öğrenme, rastgele orman, MFCC, uyku sesleri

Classification of Sleep Sounds Using MFCC Features and Adaboost Ensemble Learning Method

A regular and quality night's sleep is vital in human life. Sleep quality has a great impact on the daily lives of people and those around them. Many people today suffer from sleep disorders. Such disorders affect daily life and can impair mental health. This study proposes an approach using an ensemble learning method for the automatic classification of sleep sounds. In the study, a dataset containing 7 different sleep sounds was used. First of all, MFCC features were extracted from the audio files. Afterward, the extracted features were classified by known methods such as logistic regression, support vector machine, kNN, and random forest, which are frequently used in sound classification. In order to increase classification success, the approach of using these base classifiers together with the Adaboost ensemble learning method was proposed. An increase in classification success was observed with the proposed approach. The most successful result was obtained from the Adaboost+Random forest method with 96.439%.

Keywords:

Adaboost, ensemble learning, random forest, MFCC, sleep sounds,

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