Song Clustering Using Similarity of Audio Fingerprint
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Abstract
Listening is the most common way to detect copyright infringement or identify unknown music data, but it is difficult to analyze a large amount of music data. The accuracy also depends on the listener's level of expertise. As mentioned earlier, music recognition is applied to solve this problem and the audio fingerprint is a widely used as data feature. Audio fingerprint analysis is effective at finding audio tracks which are duplicate content (exactly match) however it cannot detect in the case of similar content. This research proposes a method for finding the similarity between two songs using relation functions for comparing audio fingerprints instead of comparing bigger music content. For a case study, we try to find the original song from the cover song to assess the efficiency of our approach. The findings of this study indicate that proposed approach can be use effectively to identify the original song covered with many genres. Overall average area under curve (Average AUC) is 0.790.
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