用频谱峰值构建轻量级音频指纹，抗极端时间拉伸。
PeakNetFP: Peak-based Neural Audio Fingerprinting Robust to Extreme Time Stretching
- 基于频谱峰值设计神经网络，借鉴点云处理思路提取特征。
- 时间拉伸50%至200%时，准确率仍超90%。
- 参数量仅为NeuralFP的1%，输入数据小11倍，适合资源受限场景。
本文提出PeakNetFP，首个专为频谱峰值设计的神经音频指纹系统。该系统借鉴计算机视觉中的PointNet++思想，采用分层点特征提取，并通过对比学习训练，性能接近当前最先进的NeuralFP。在大量测试中，对于50%至200%的时间拉伸，其Top-1命中率保持在90%以上。此外，相比NeuralFP，PeakNetFP参数量减少100倍，输入数据规模缩小11倍，具备显著效率优势。该方法成功融合了传统峰值法的轻量化与神经网络的强泛化能力，为未来高效音频指纹技术提供了新方向。
原文摘要 · Abstract (English)
This work introduces PeakNetFP, the first neural audio fingerprinting (AFP) system designed specifically around spectral peaks. This novel system is designed to leverage the sparse spectral coordinates typically computed by traditional peak-based AFP methods. PeakNetFP performs hierarchical point feature extraction techniques similar to the computer vision model PointNet++, and is trained using contrastive learning like in the state-of-the-art deep learning AFP, NeuralFP. This combination allows PeakNetFP to outperform conventional AFP systems and achieves comparable performance to NeuralFP when handling challenging time-stretched audio data. In extensive evaluation, PeakNetFP maintains a Top-1 hit rate of over 90% for stretching factors ranging from 50% to 200%. Moreover, PeakNetFP offers significant efficiency advantages: compared to NeuralFP, it has 100 times fewer parameters and uses 11 times smaller input data. These features make PeakNetFP a lightweight and efficient solution for AFP tasks where time stretching is involved. Overall, this system represents a promising direction for future AFP technologies, as it successfully merges the lightweight nature of peak-based AFP with the adaptability and pattern recognition capabilities of neural network-based approaches, paving the way for more scalable and efficient solutions in the field.
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