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Austrian Academy of Sciences

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Research library15linked papers
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Selected work

Representative Papers

Training Set Synthesis for Bioacoustic Denoising: A Case Study With Mice

Aug 10, 2026

This study addresses the challenge of denoising bioacoustic recordings corrupted by environmental noise, where clean reference signals are typically unavailable for supervised training. To circumvent the need for real clean data, the authors propose a self-supervised approach that synthesizes training samples containing fundamental frequency and harmonic ridges. They develop a U-Net-based model to predict complex ratio masks and introduce a ridge-guided weighted loss function to better preserve fine-grained vocal structure during denoising. Evaluated on murine ultrasonic vocalizations, the method significantly improves the accuracy of fundamental frequency and harmonic tracking, enhances scale-invariant signal-to-noise ratio, and boosts the generalization performance of downstream classifiers in noisy field conditions.

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Approximation Rates for Metaplectic Neural Networks

Aug 09, 2026

This work addresses the challenge that conventional neural networks struggle to effectively approximate solutions to problems with symplectic structures or quantum dynamics, such as the time-dependent Schrödinger equation. It introduces the metaplectic transform into neural network theory for the first time, constructing a neural dictionary based on this transform and defining a corresponding metaplectic Barron space. The study establishes embedding relations between this space and Sobolev spaces, providing a theoretical foundation for a novel deep network architecture. This architecture leverages finite linear combinations to achieve Monte Carlo approximation of metaplectic Barron functions. Numerical experiments demonstrate that the proposed method significantly outperforms classical physics-informed neural networks in solving the time-dependent Schrödinger equation, thereby validating the expressive power and effectiveness of the introduced dictionary.

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Recent publications

Latest Papers

Training Set Synthesis for Bioacoustic Denoising: A Case Study With Mice

Aug 10, 2026

This study addresses the challenge of denoising bioacoustic recordings corrupted by environmental noise, where clean reference signals are typically unavailable for supervised training. To circumvent the need for real clean data, the authors propose a self-supervised approach that synthesizes training samples containing fundamental frequency and harmonic ridges. They develop a U-Net-based model to predict complex ratio masks and introduce a ridge-guided weighted loss function to better preserve fine-grained vocal structure during denoising. Evaluated on murine ultrasonic vocalizations, the method significantly improves the accuracy of fundamental frequency and harmonic tracking, enhances scale-invariant signal-to-noise ratio, and boosts the generalization performance of downstream classifiers in noisy field conditions.

0 citationsRead paper

Approximation Rates for Metaplectic Neural Networks

Aug 09, 2026

This work addresses the challenge that conventional neural networks struggle to effectively approximate solutions to problems with symplectic structures or quantum dynamics, such as the time-dependent Schrödinger equation. It introduces the metaplectic transform into neural network theory for the first time, constructing a neural dictionary based on this transform and defining a corresponding metaplectic Barron space. The study establishes embedding relations between this space and Sobolev spaces, providing a theoretical foundation for a novel deep network architecture. This architecture leverages finite linear combinations to achieve Monte Carlo approximation of metaplectic Barron functions. Numerical experiments demonstrate that the proposed method significantly outperforms classical physics-informed neural networks in solving the time-dependent Schrödinger equation, thereby validating the expressive power and effectiveness of the introduced dictionary.

0 citationsRead paper