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Radio Broadcast Monitoring to Ensure Copyright Ownership

DOI: 10.4038/icter.v11i1.7193

Keywords: Audio fingerprint,Features extraction,Playlist generation,Wavelets,Broadcast monitoring,Silence detection

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Abstract:

In this research, we provide a way to protect copyright ownership of multimedia objects like songs. Protecting ownership is very important when we are working in a corporative environment. If someone tries to misuse others’ property it is an illegal action. But capturing theses kind of actions especially on electronic objects are very difficult. There are several ways to share copyright ownership with others. We can purchase others’ property and use them under conditions provided by the owner. In this research, we are focusing on protecting ownership of audio songs broadcasted by radio channels. There are so many radio channels in a country, as well as a huge number of songs are broadcasted per day. In order to broadcast particular song in a radio channel, they should purchase the right to do so. In order to make sure that this process is functioning correctly, we have to monitor radio channel and extract the broadcasted songs. Currently, this is happened manually. Manual process is no longer able to continue because the number of songs is increasing day by day. We provide an automated solution for this real-world problem. There are so many researches on this area done by various researches. They used different approaches to achieve the final goal. Most of them have used content base audio identification base approaches. In this context, an unknown audio file is identified by analysing the content of it. But, it is very hard to find researches that use this concept to monitor radio channel since there are additional complexities when we come to monitor radio channel. In this research, we extend the content-based audio identification approach to monitor radio channel automatically. First, we perform a pre-process on raw audio objects that broadcasted by a radio channel. After extracting interest areas, we generate set of hash values. Then, we perform powerful approximate matching against pre-stored hashes. Ultimately, we provide a detailed report which contains all the information of broadcasted audio songs

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