This project describes the necessary code to implement an EEG-based emotion recognition using SincNet [Ravanelli & Bengio 2018] including data from individuals diagnosed with Autism (ASD). For more details and data request send an email to the authors and contributors Juan Manuel Mayor Torres (juan.mayortorres@unitn.it) and Mirco Ravanelli (Mila)
Companion repository for the paper "A Comparison of Metric Learning Loss Functions for End-to-End Speaker Verification" published at SLSP 2020
Universal Adversarial Audio Perturbations
An Implementation of SincNet using Tenorflow 2.x.
SincNet with Attention Mechanism using PyTorch
Deep learning system for seismic velocity inversion using a SincNet-GAT-UNet architecture, physics-informed temporal encoding, graph attention fusion, and FiLM-conditioned spatial prediction
Keras implementation of SincNet from "Speaker Recognition from Raw Waveform with SincNet".
CNN on raw waveforms
Invertible spectrograms for PyTorch: audio → perceptual spectrogram (mel/Bark/ERB) → audio. Lightweight, interpretable, CPU-fast.
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