4 papers
LipsAM: Lipschitz-Continuous Amplitude Modifier for Audio Signal Processing and its Application to Plug-and-Play Dereverberation
Kazuki Matsumoto, Ren Uchida, Kohei Yatabe
The robustness of deep neural networks (DNNs) can be certified through their Lipschitz continuity, which has made the construction of Lipschitz-continuous DNNs an active research f…
Learn and Verify: A Framework for Rigorous Verification of Physics-Informed Neural Networks
Kazuaki Tanaka, Kohei Yatabe
The numerical solution of differential equations using neural networks has become a central topic in scientific computing, with Physics-Informed Neural Networks (PINNs) emerging as…
Local Equivariance Error-Based Metrics for Evaluating Sampling-Frequency-Independent Property of Neural Network
Kanami Imamura, Tomohiko Nakamura, Norihiro Takamune +2
Audio signal processing methods based on deep neural networks (DNNs) are typically trained only at a single sampling frequency (SF) and therefore require signal resampling to handl…
Subband Splitting: Simple, Efficient and Effective Technique for Solving Block Permutation Problem in Determined Blind Source Separation
Kazuki Matsumoto, Kohei Yatabe
Solving the permutation problem is essential for determined blind source separation (BSS). Existing methods, such as independent vector analysis (IVA) and independent low-rank matr…