Automatic Classification of Environmental Sounds with the MFCC Method and the Proposed Deep Model

Yıl 2022, Cilt: 34 Sayı: 1, 449 - 457, 20.03.2022

Muhammed Yıldırım

https://doi.org/10.35234/fumbd.1056326

Cited By: 5

Öz

With the developing technology, the Internet of Things (IoT) is at the forefront of bringing different technologies together. The Internet of Things is also frequently used, especially in smart city applications. Smart city applications are becoming more common day by day. In this study, an application that will be used frequently in smart city applications has been realized. In this study, the UrbanSound8K dataset, which consists of environmental sounds and is one of the largest datasets in the literature, was used. A new deep one-dimensional (1D-CNN) model is proposed to classify environmental sounds to contribute to smart city applications. In the developed model, firstly, the feature maps of the environmental sounds in the data set were obtained by using the MFCC method. A high accuracy value was obtained when the feature maps obtained later were classified in the developed 1D-CNN network. This accuracy value obtained shows that the proposed model can be used in the classification process of audio data.

Anahtar Kelimeler

Classification, MFCC, Deep Learning, IoT, Smart Cities

MFCC Yöntemi ve Önerilen Derin Model ile Çevresel Seslerin Otomatik Olarak Sınıflandırılması

Öz

Gelişen teknoloji ile birlikte Nesnelerin İnterneti (IoT), farklı teknolojileri bir araya getirmenin ön saflarında yer almaktadır. Nesnelerin interneti özellikle akıllı şehir uygulamalarında da sıklıkla kullanılmaktadır. Akıllı şehir uygulamaları her geçen gün daha da yaygın bir hale gelmektedir. Yapılan bu çalışmada da akıllı şehir uygulamalarında sıklıkla kullanılacak bir uygulama gerçekleştirilmiştir. Bu çalışmada çevre seslerinden oluşan ve bu konuda literatürdeki en büyük veri setlerinden biri olan UrbanSound8K veri seti kullanılmıştır. Akıllı şehir uygulamalarına katkıda bulunmak amacıyla çevresel sesleri sınıflandırmak için yeni bir derin tek boyutlu (1D-CNN) model önerilmiştir. Geliştirilen modelde ilk olarak MFCC yöntemi kullanılarak veri setindeki çevresel seslerin öznitelik haritaları elde edilmiştir. Daha sonra elde edilen öznitelik haritaları geliştirilen 1D-CNN ağında sınıflandırıldığında yüksek bir doğruluk değeri elde edilmiştir. Elde edilen bu doğruluk değeri önerilen modelin ses verilerini sınıflandırma işleminde kullanılabileceğini göstermektedir.

Anahtar Kelimeler

Sınıflandırma, MFCC, Derin Öğrenme, IoT, Akıllı Şehirler

Kaynakça

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