Sound Scene and Events Detection using Deep Learning in the Scope of Cyber Security for Multimedia Systems

Year 2019, Volume: 3 Issue: 2, 60 - 82, 30.12.2019

Bahadir Karasulu

Abstract

In addition to many natural sound sources in nature, synthetic sounds are also used in the multimedia systems of our modern world. Environments (i.e., sound scenes) with these sounds are important for biometric authorization, security requirements and robust/safer voice/video communication. Apart from audio formats that have special constraints such as speech/speaker recognition and verification, the separation of polyphonic sounds, noise reduction, detection of sound scenes/events and voice tagging processes are gaining importance in order to create safer information systems in terms of cyber security. In recent years deep learning has been preferred in the field of cyber security due to its layered infrastructure, which enables the easy extraction of attributes and semantic relationships in the raw data. In this study, the use of deep learning architecture models for voice (or speech) analysis and classification/prediction and detection as multimedia data in cyber security coverage is examined. In our study, deep neural networks, convolutional neural networks, recurrent neural networks, restricted Boltzmann machine and deep belief networks are systematically reviewed as prominent models in the publications between 2015 and 2019. Therefore, the orientation in the literature on voice/speech processing in cyber security, prevention of voice spoofing, and achieving consistent and high performance results is clearly demonstrated through discussions and comments based on scientific findings over fourty studies. 

Keywords

Speaker Recognition, Cyber Security, Sound Event Detection, Deep Learning

Çoklu Ortam Sistemleri İçin Siber Güvenlik Kapsamında Derin Öğrenme Kullanarak Ses Sahne ve Olaylarının Tespiti

Abstract

Günümüzde doğadaki birçok doğal ses kaynağı yanısıra sentetik sesler de çoklu ortam sistemlerinde kullanılmaktadır. Bu seslerin bulunduğu ortamlar (sahneler) biyometrik yetkilendirme, güvenlik isterleri ve gürbüz/güvenli sesli/görüntülü iletişim için önem arz etmektedir. Konuşma/konuşmacı tanıma, doğrulama gibi özel kısıtlara sahip ses biçemleri haricinde çoklu seslerin ayrıştırılması, gürültü giderilmesi, ses sahnesi/ olaylarının tespiti ve ses etiketleme işlemleri siber güvenlik açısından daha güvenli bilişim sistemleri oluşturulması adına gün geçtikçe önem kazanmaktadır. Derin öğrenme katmanlı altyapısı gereği oldukça iyi bir biçimde ham verideki özniteliklerin ve anlamsal ilişkinin elde edilmesine olanak sunmasından dolayı son yılllarda siber güvenlik alanında da tercih edilir olmuştur. Bu çalışmada siber güvenlik kapsamında çoklu ortam verisi olarak ses (veya konuşma) analizi ve sınıflandırma/tahminleme ve tespit için derin öğrenme mimari modellerinin kullanımı irdelenmiştir. Çalışmamızda 2015 ilâ 2019 yılları arasındaki yayınlarda öne çıkan modeller olan derin sinir ağları, evrişimli sinir ağları, tekrarlayıcı sinir ağları, kısıtlanmış Boltzmann makinesi ve derin inanç ağları sistematik olarak incelenmiştir. Böylece siber güvenlikte ses/konuşma işleme, sesle aldatmayı önleme, tutarlı ve yüksek başarımlı sonuçları elde etmeye dair literatürdeki yönelim kırkı aşkın çalışma üzerinden bilimsel bulgulara dayanan tartışma ve yorumlarla açıkça ortaya konulmaktadır.

Keywords

Konuşmacı Tanıma, Siber Güvenlik, Ses Olayı Tespiti, Derin Öğrenme

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