Tootfinder

Contrastive and Transfer Learning for Effective Audio Fingerprinting through a Real-World Evaluation Protocol
Christos Nikou, Theodoros Giannakopoulos
https://arxiv.org/abs/2507.06070

Contrastive and Transfer Learning for Effective Audio Fingerprinting through a Real-World Evaluation Protocol
Recent advances in song identification leverage deep neural networks to learn compact audio fingerprints directly from raw waveforms. While these methods perform well under controlled conditions, their accuracy drops significantly in real-world scenarios where the audio is captured via mobile devices in noisy environments. In this paper, we introduce a novel evaluation protocol designed to better reflect such real-world conditions. We generate three recordings of the same audio, each with incre…

Machine Learning Integrated Near-Infrared Surface-Enhanced Raman Spectroscopy for Accurate Strain-Level Virus Identification
Na Zhang, Ziyang Wang, Xielin Wang, Gabriel A. V\'azquez-Lizardi, Paula Pi\~neiro Varela, Dorleta Jimenez de Aberasturi, David E. Sanchez, Nestor Perea-Lopez, Samuel Lin, Ryeanne Ricker, Edgar Dimitrov, Alexander J. Sredenschek, Kalana D. Halanayake, Yin-Ting Yeh, Julian A. Mintz, Jiarong Ye, Sharon Xiaolei Huang, Huaguang Lu, Elodie Ghedin, Danielle Reifsnyd…

Machine Learning Integrated Near-Infrared Surface-Enhanced Raman Spectroscopy for Accurate Strain-Level Virus Identification
Strain-level identification of viruses is critical for effective public health responses to potential outbreaks, yet current diagnostic methods often lack the necessary speed or sensitivity. Surface-enhanced Raman spectroscopy (SERS) offers great potential for fast and precise virus clarification through unique vibrational fingerprints of biological components. However, existing protocols typically operate outside of the tissue's transparent near-infrared (NIR) window, and are further limited b…