Assessing the Suitability of Panako for Music Identification in Movies

Abstract

Audio fingerprinting is a technique that allows for fast identification of music. Research concerning this technique first emerged around the 2000s and has lead to several applications, like Shazam. More recently, developments in this area have slowed down, even though there are still new challenges emerging. This paper investigates one of these challenges, music identification of movies, in a systematic way using the open-source fingerprinting framework called Panako. First, clips containing music were extracted from movies and queried using the default settings for Panako. Then, movie soundtracks were modified by layering noise over them, or by time-stretching and pitch-shifting them, and the performance of Panako on these modified audio signals was evaluated using a benchmark. Finally, the best configurations from the previous step were again queried using actual movie clips. These tests showed that both the default configuration and the configurations that performed best on the synthesised data perform poorly in movie music identification. Less than 10\% of the clips were identified correctly. The limited scope of this research, combined with the results gathered, show that there should be further investigation into the suitability of Panako for movie music identification.