Tootfinder

Stereo sound event localization and detection based on PSELDnet pretraining and BiMamba sequence modeling
Wenmiao Gao, Yang Xiao
https://arxiv.org/abs/2506.13455

Stereo sound event localization and detection based on PSELDnet pretraining and BiMamba sequence modeling
Pre-training methods have achieved significant performance improvements in sound event localization and detection (SELD) tasks, but existing Transformer-based models suffer from high computational complexity. In this work, we propose a stereo sound event localization and detection system based on pre-trained PSELDnet and bidirectional Mamba sequence modeling. We replace the Conformer module with a BiMamba module and introduce asymmetric convolutions to more effectively model the spatiotemporal …

Using deep learning to characterize single-exposure double-line spectroscopic binaries
Avraham Binnenfeld, Samuel Lilek, Rami Nasser, Raja Giryes, Shay Zucker
https://arxiv.org/abs/2507.12363

Using deep learning to characterize single-exposure double-line spectroscopic binaries
Distinguishing the component spectra of double-line spectroscopic binaries (SB2s) and extracting their stellar parameters is a complex and computationally intensive task that usually requires observations spanning several epochs that represent various orbital phases. This poses an especially significant challenge for large surveys such as Gaia or LAMOST, where the number of available spectra per target is often not enough for a proper spectral disentangling. We present a new approach for charac…

Enhancing Stereo Sound Event Detection with BiMamba and Pretrained PSELDnet
Wenmiao Gao, Han Yin
https://arxiv.org/abs/2507.09570 https://

Enhancing Stereo Sound Event Detection with BiMamba and Pretrained PSELDnet
Pre-training methods have greatly improved the performance of sound event localization and detection (SELD). However, existing Transformer-based models still face high computational cost. To solve this problem, we present a stereo SELD system using a pre-trained PSELDnet and a bidirectional Mamba sequence model. Specifically, we replace the Conformer module with a BiMamba module. We also use asymmetric convolutions to better capture the time and frequency relationships in the audio signal. Test…