GSTF Journal on Computing (JoC)

, 3:9

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

A Highly Robust Audio Monitoring System for Radio Broadcasting

  • E.D. NishanAffiliated withUniversity of Colombo School of Computing
  • , W. SenevirathnaAffiliated withUniversity of Colombo School of Computing
  • , K.L. JayaratneAffiliated withUniversity of Colombo School of ComputingInformation Technology, University of Western Sydney


Proposing a novel approach for monitoring songs for the radio broadcasting channels is very important for the interest of singers, writers and musicians in the musical industry. Singers, writers and musicians have a claim to intellectual property rights for their songs broadcast over all the radio channels. According to this intellectual property rights act singers, writers and musicians should be paid for their songs broadcast over all the radio channels. Therefore we propose a real time audio monitoring approach to solve this problem which includes our own audio recognition algorithm. It is easy to recognize a song, when you provide the original high quality blueprint of the song as input. But we can’t expect such kind of audio input from radio channels since lots of transformations are possible before reaching the end user or listener. For example, adding environmental effects such as noise, adding commercials on the song as watermarks, playing more than one song as a chain without adding any silence between them, playing a part of the song, playing same song in various speeds and so on. These transformations cause change in the uniqueness of particular song and make the problem even more difficult. The algorithm we proposing is resistant to noise and distortion as well as it is capable of recognizing short segment of song when broadcasting over the radio channels. At the end of the processing our system generates a descriptive report including title of the song, singer of the song, writer of the song, composer of the song, number of times it was played and when it was played for all songs for a particular period for all radio broadcasting channels. We evaluate our system against various types of real time scenarios and achieved overall higher level of accuracy (96%) at the end.


Audio fingerprint features extraction playlist generation wavelets broadcast monitoring