Advancing Audio Fingerprinting Accuracy Addressing Background Noise and
  Distortion Challenges
Navin Kamuni Sathishkumar Chintala Naveen Kunchakuri Jyothi Swaroop Arlagadda Narasimharaju Venkat Kumar
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.