Variable-Length Audio Fingerprinting
Source: arXiv:2603.23947v1
Announcement Type: Cross
Abstract: Audio fingerprinting converts audio to much lower-dimensional representations, allowing distorted recordings to still be recognized as their originals through similar fingerprints. Existing deep learning approaches rigidly fingerprint fixed-length audio segments, thereby neglecting temporal dynamics during segmentation. To address limitations due to this rigidity, we propose Variable-Length Audio FingerPrinting (VLAFP), a novel method that supports variable-length fingerprinting. To the best of our knowledge, VLAFP is the first deep audio fingerprinting model capable of processing audio of variable length, for both training and testing. Our experiments show that VLAFP outperforms existing state-of-the-arts in live audio identification and audio retrieval across three real-world datasets.
Introduction to Audio Fingerprinting
Audio fingerprinting is a technology that enables the identification of audio content by converting it into a compact representation. This process is crucial for various applications, including music identification, copyright enforcement, and content-based audio retrieval.
The Challenge of Fixed-Length Segmentation
Traditional audio fingerprinting methods rely on fixed-length segments of audio, which may overlook the nuances of temporal dynamics in sound. This rigidity can hinder the performance of these systems, particularly in real-world scenarios where audio content can vary significantly in length and quality.
Introducing VLAFP
The Variable-Length Audio FingerPrinting (VLAFP) method addresses these limitations by allowing for variable-length audio segments to be fingerprinted. This flexibility not only enhances the model’s adaptability to different types of audio but also improves its robustness in recognizing distorted or altered audio recordings.
Key Features of VLAFP
- Variable-Length Processing: Unlike traditional models, VLAFP can handle audio of varying lengths during both the training and testing phases.
- Improved Recognition Rates: Experiments demonstrate that VLAFP significantly outperforms existing state-of-the-art methods in live audio identification.
- Real-World Applicability: VLAFP has been evaluated across three real-world datasets, showcasing its effectiveness in diverse scenarios.
Experimental Results
The performance of VLAFP was tested against several benchmarks in the field of audio fingerprinting. The results indicate superior accuracy and efficiency, particularly in environments with noisy or distorted audio inputs.
Conclusion
Variable-Length Audio FingerPrinting marks a significant advancement in the field of audio recognition technologies. By overcoming the constraints of fixed-length audio processing, VLAFP opens new avenues for research and practical applications in music recognition, audio analysis, and beyond. As the demand for more sophisticated audio identification systems grows, innovations like VLAFP will play a crucial role in shaping the future of audio technology.