Konuşma/Müzik Ayrıştırması için Kesikli Dalgacık Dönüşümü Tabanlı Öznitelik Çıkarımı

Year 2015, Volume: 30 Issue: 2, 103 - 114, 25.07.2016

Timur Düzenli Hatice Doğan Nalan Özkurt

https://doi.org/10.21605/cukurovaummfd.242769

Abstract

Bu çalışmada konuşma ve müzik işaretlerinin birbirinden ayrıştırılabilmesi için kesikli dalgacık dönüşümü tabanlı bir öznitelik seti önerilmiştir. Öznitelik setinde dalgacık katsayılarının ortalamaları, varyansları ve altbandlar arası değişim oranları kullanılmıştır. Dalgacık dönüşümünün sinyalleri iyi ifade edebilmesi sayesinde, 0,5 saniyelik pencerelerde dahi yüksek doğruluklu bir sınıflandırma sağlanabilmiştir. Veri seti olarak internet radyolarından kaydedilmiş çeşitli bayan-erkek konuşmaları ve farklı türlerden müzik işaretleri kullanılmıştır. Daubechies-8 dalgacığının yok etme moment sayısı ve dikgenliği dikkate alındığında bu ailenin diğer üyeleri arasında en iyi performansa sahip olduğu gözlenmiştir. Öznitelikler çıkarıldıktan sonra, ilintili öznitelikleri yok etmek için temel bileşen analizi kullanılmıştır. Sınıflandırma hem yapay sinir ağları hem de destek vektör makineleri ile yapılmış ve önerilen özniteliklerin, klasik özniteliklerden çok daha iyi performans gösterdiği gözlenmiştir

Keywords

Konuşma/müzik ayrıştırma, Ayrık dalgacık dönüşümü, Destek vektör makineleri

Discrete Wavelet Transform Based Feature Extraction for Speech/Music Discrimination

Abstract

In this study, a discrete wavelet transform based feature set has been proposed for discrimination of music and speech. The feature set is constructed using the mean and variances of discrete wavelet coefficients and ratio of the change between the wavelet subbands. Due to the good representation ability of the wavelets, a high accuracy classification can be obtained even for a short window of 0,5 seconds. A database which contains a wide variety of radio recordings from internet radios with different male and female speakers and various genres of musical pieces is constructed. The best performance is obtained with Daubechies-8 wavelet among the other members of the Daubechies family, considering the number of vanishing moments and orthogonality. The principal component analysis has been applied to eliminate the correlated features. The classification has been accomplished using both artificial neural networks and support vector machines and according to the results the proposed feature set outperforms the traditional ones

Keywords

Speech/music discrimination, Discrete wavelet transform, Support vector machines

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