Advancing Audio Fingerprinting Accuracy Addressing Background Noise and Distortion Challenges

arXiv:2402.13957v1 Announce Type: cross
Abstract: Audio fingerprinting, exemplified by pioneers like Shazam, has transformed digital audio recognition. However, existing systems struggle with accuracy in challenging conditions, limiting broad applicability. This research proposes an AI and ML integrated audio fingerprinting algorithm to enhance accuracy. Built on the Dejavu Project's foundations, the study emphasizes real-world scenario simulations with diverse background noises and distortions. Signal processing, central to Dejavu's model, includes the Fast Fourier Transform, spectrograms, and peak extraction. The "constellation" concept and …

abstract accuracy ai and ml algorithm arxiv audio challenges cs.lg cs.sd digital eess.as noise project recognition research shazam struggle systems type