Music Genre Determination Using Audio Fingerprinting

This paper compares two audio spotting methods: a feature-based audio classification method and a hashing method for audio fingerprinting. Moreover, the music genre determination performance of the methods is investigated. In this context, advantages and disadvantages of these methods are discussed and the audio identification performance of both methods are reported. Robustness to a number of attacks is also investigated. It is concluded that the performance of audio fingerprinting system outperforms the feature-based classification under the i.i.d. noise, mp3 compression and resampling attacks. However, the feature-based classification provides a higher detection accuracy under the time compression attack. Both systems are robust to synchronization attacks which is important for broadcast monitoring applications.