标题： PeakNetFP：基于峰值的鲁棒极端时间拉伸的神经音频指纹 显示英文标题

标题： PeakNetFP: Peak-based Neural Audio Fingerprinting Robust to Extreme Time Stretching

摘要： 这项工作介绍了PeakNetFP，第一个专门围绕频谱峰值设计的神经音频指纹（AFP）系统。 该创新系统旨在利用传统基于峰值的AFP方法通常计算的稀疏频谱坐标。 PeakNetFP执行类似于计算机视觉模型PointNet++的分层点特征提取技术，并像最先进的深度学习AFP NeuralFP一样使用对比学习进行训练。 这种组合使PeakNetFP优于传统的AFP系统，并在处理具有挑战性的时延音频数据时达到与NeuralFP相当的性能。 在广泛的评估中， PeakNetFP对于从50%到200%的时延因子保持超过90%的Top-1命中率。 此外，PeakNetFP具有显著的效率优势：与NeuralFP相比，它的参数少100倍，使用的输入数据量小11倍。 这些特性使PeakNetFP成为涉及时延的AFP任务的一个轻量且高效的解决方案。 总体而言，该系统代表了未来AFP技术的一个有前景的方向，因为它成功地将基于峰值的AFP的轻量特性与基于神经网络的方法的适应性和模式识别能力结合在一起，为该领域更可扩展和高效的解决方案铺平了道路。 显示英文摘要

摘要： 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.