SincNet is a neural architecture for efficiently processing raw audio samples.
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Updated
Apr 28, 2021 - Python
SincNet is a neural architecture for efficiently processing raw audio samples.
Fast audio data augmentation in PyTorch. Inspired by audiomentations. Useful for deep learning.
Functions and scripts for analyzing waveforms, primarily audio. This is currently somewhat disorganized and unfinished.
Open-source, license-free MCP server for RTL waveform debug: reads FST waveforms (VCD/FSDB auto-convert) plus a SystemVerilog netlist, with 34 tools covering driver analysis, value/X tracing, pass-fail waveform diff, a browser wave viewer the agent drives, and pre-simulation static analysis.
WavTTS: Towards High-Quality Zero-Shot TTS via Direct Raw Waveform Modeling
Open-source, evidence-driven MCP server for RTL simulation debugging: correlate VCS/Xcelium logs, VCD/FSDB waveforms, SystemVerilog/UVM source, hierarchy, and connectivity to trace failures to root cause.
Tensorflow - Very Deep Convolutional Neural Networks For Raw Waveforms - https://arxiv.org/pdf/1610.00087.pdf
Keras (tensorflow) implementation of SincNet (Mirco Ravanelli, Yoshua Bengio - https://github.com/mravanelli/SincNet)
This repository provides the code used to create the results presented in "Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles".
A python script to process raw ECG signals and impute the peaks and heartbeats following noise suppression to obtain processed ECG
Pytorch Reimplementation of DiffWave unconditional generation: a high quality waveform synthesizer.
A Python signal analysis toolbox for computing spectral-domain interactions using the bispectrum.
VCD visualizer: view your waveforms in ASCII format, or export them to TikZ figures.
Fetch seedlink data and store them into InfluxDB
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