Kızılgerdan Kuş Popülasyonu Biyoakustik Kayıtlarının Takibi İçin Kuş Sesi Tanıma Yöntemi Geliştirilmesi

Abstract

Bu çalışmada, Kızılgerdan kuş popülasyonuna ait dört alt türün biyoakustik özelliklerinden tespiti için uygun öznitelik ve sınıflandırma yöntemi araştırılmıştır. Özniteliklerin belirlenmesi için Mel Frekansı Kepstrum Katsayıları temel alınmış ve bu katsayılardan istatistiksel parametreler yardımıyla hesaplanabilecek uygun öznitelik araştırması yapılmıştır. Sınıflandırma aşamasında Doğrusal Ayırma Ayıracı, Destek Vektör Makineleri ve k-En Yakın Komşuluk ve Ardışıl İleri Yönlü Öznitelik yöntemleri kullanılmıştır. Sınıflandırıcı parametreleri 10-kat çapraz doğrulama yöntemi ile eğitim setinde belirlenmiştir. Daha sonra, eğitilmiş sınıflandırıcı parametreleri test veri setine uygulanarak sınıflandırma doğruluğu elde edilmiştir. Sonuç olarak, çalışmamızda Mel Frekansı Kepstrum katsayıları temel alınarak hesaplanan ortalama, etkinlik ve karmaşıklık parametreleri k-En Yakın Komşuluk Yöntemi ile sınıflandırıldığında en iyi başarım elde edilmiştir. Önerdiğimiz yöntemin sınıflandırma başarımı eğitim kümesinde %97, test kümesinde ise %94 olarak elde edilmiştir.

Keywords

• Kuş sesi sınıflandırma,Mel frekansı kepstrum katsayısı,İstatistiksel parametreler

Developing Bird Song Recognition Method for Monitoring Robin Birds Population Bioacoustics Records

Abstract

In this study, suitable features and classification methods were investigated to determine the four subspecies of Robin birds population from their bioacoustic characteristics. Mel Frequency Cepstrum Coefficients were taken as basis for the determination of the features and a suitable feature search was performed by using statistical parameters from these coefficients. In the classification stage Linear Discriminant Analysis, Support Vector Machines, k-Nearest Neighborhood, and Sequential Forward Feature Selection methods were used. Classifier parameters were determined by 10-fold cross validation method. Then, the classification accuracy was obtained by applying the trained classifier parameters to the test data set. As a result, in our study, the best performance was obtained when the mean, efficiency and complexity parameters, which were calculated based on Mel Frequency Kepstrum coefficients, were classified by k-Nearest Neighborhood Method. The classification performance of the proposed method was obtained 97% in the training set and 94% in the test set.

Keywords

• Bird song classification,Mel frequency cepstrum coefficients,Statistical parameters

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