# Advancing Audio Fingerprinting Accuracy Addressing Background Noise and Distortion Challenges

arXiv: https://arxiv.org/abs/2402.13957 · HTML: https://mlsanity.com/paper/2402.13957/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning](https://mlsanity.com/paper/2010.11910/) (2020)
- [Robust and lightweight audio fingerprint for Automatic Content Recognition](https://mlsanity.com/paper/2305.09559/) (2023)

## Built on by (most cited first, 3 of 3)

- [The Emotional Impact of Game Duration: A Framework for Understanding Player Emotions in Extended Gameplay Sessions](https://mlsanity.com/paper/2404.00526/) (2024)
- [A Novel Audio Representation for Music Genre Identification in MIR](https://mlsanity.com/paper/2404.01058/) (2024)
- [Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets](https://mlsanity.com/paper/2404.07225/) (2024)

## Abstract (arXiv)

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.