Reverberation Effect on Online Hazardous Sound Event Detection

Year 2022, Volume: 02 Issue: 01, 29 - 39, 31.07.2022

Yüksel Arslan

Abstract

Bu makale, tehlikeli Ses Olayı Algılama (SED) ile ilgili araştırmanın sonuçlarını bildirmektedir. Araba kazalarını ve çığlıkları tespit etmek için Derin Sinir Ağlarını (DNN) kullandık. Güvenlik için sesli izleme uygulamalarının bu sesleri algılaması beklenir. Bu seslerin çevrim dışı (kayıtlı) veriler üzerinden tespiti konusunda birçok araştırma yapılmıştır ancak bu seslerin çevrim içi tespiti önemlidir. Araştırmamızın, kayıtlı veriler üzerinde tespit ve aynı veriler üzerinde çevrimiçi tespit yapıp sonuçları karşılaştırmamıza katkısı vardır. Çevrimiçi tespit sırasında, gözetim uygulamaları için tehlikeli SED uygulamak isteyen uygulayıcılara yardımcı olacak bazı önemli noktaları fark ettik. Testlerimiz, uzak hoparlör tanımada (DSR) karşılaşılan sorunların tehlikeli SED'de de görüldüğünü göstermektedir. SED ile ilgili mevcut araştırmalar, genellikle model geliştirme sırasında arka plan gürültüsünü hesaba katar. Çevrimiçi testlerimiz, yankılanmanın performansı önemli ölçüde düşürebileceğini gösteriyor. Çevrimiçi testlerin sonuçları olarak, gerçek dünya uygulamalarında kullanım için tehlikeli bir SED geliştirme modeli için bazı önerilerde bulunuyoruz.

Keywords

sesli gözetleme, tehlikeli ses olayı algılama, derin sinir ağları, yankılanma

Reverberation Effect on Online Hazardous Sound Event Detection

Abstract

This paper reports the results of the research on hazardous Sound Event Detection (SED). We used Deep Neural Networks (DNN) to detect car crashes and screams. These are the two of the hazardous sound events on which studies are done for detection. We have selected these sounds because detection of these sounds and early warning can save lives. The research made on hazardous sound events are generally on recorded data. In this paper we wanted to show that there is a difference between recorded data and online (playing) data. At the end if an audio surveillance algorithm would be used in real time, to test it with online data was also an important part of the development. In this research we have developed an online detection environment which consists of a database, automatic audio playing and receiving software, detection software and automatic evaluating software. Our tests show that the reverberation degrades performance significantly. Current research on SED usually only takes into account background noise which is inserted artificially during model development. The results we have found during these online tests are the same as the ones we encountered during far field speaker recognition.

Keywords

audio surveillance, hazardous sound event detection, deep neural networks, reverberation

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