Advancing Audio Fingerprinting Accuracy Addressing Background Noise and Distortion Challenges
This work addresses accuracy limitations in audio recognition systems for real-world applications, though it appears incremental as it builds on existing foundations like the Dejavu Project.
The research tackled the problem of audio fingerprinting accuracy under challenging conditions like background noise and distortion, achieving 100% accuracy with a 5-second audio input.
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 fingerprint hashing enable unique song identification. Performance evaluation attests to 100% accuracy within a 5-second audio input, with a system showcasing predictable matching speed for efficiency. Storage analysis highlights the critical space-speed trade-off for practical implementation. This research advances audio fingerprinting's adaptability, addressing challenges in varied environments and applications.