Advancing Audio Fingerprinting Accuracy Addressing Background Noise and Distortion Challenges
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arXiv
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| Format: | Preprint |
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2024
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| author | Kamuni, Navin Chintala, Sathishkumar Kunchakuri, Naveen Narasimharaju, Jyothi Swaroop Arlagadda Kumar, Venkat |
| author_facet | Kamuni, Navin Chintala, Sathishkumar Kunchakuri, Naveen Narasimharaju, Jyothi Swaroop Arlagadda Kumar, Venkat |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13957 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancing Audio Fingerprinting Accuracy Addressing Background Noise and Distortion Challenges Kamuni, Navin Chintala, Sathishkumar Kunchakuri, Naveen Narasimharaju, Jyothi Swaroop Arlagadda Kumar, Venkat Sound Machine Learning Audio and Speech Processing 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. |
| title | Advancing Audio Fingerprinting Accuracy Addressing Background Noise and Distortion Challenges |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2402.13957 |