There are 85 repositories under digital-signal-processing topic.
SincNet is a neural architecture for efficiently processing raw audio samples.
Theory of digital signal processing (DSP): signals, filtration (IIR, FIR, CIC, MAF), transforms (FFT, DFT, Hilbert, Z-transform) etc.
Digital Signal Processing - Theory and Computational Examples
Overview of the peaks dectection algorithms available in Python
Large collection of number systems providing custom arithmetic for mixed-precision algorithm development and optimization for AI, Machine Learning, Computer Vision, Signal Processing, CAE, EDA, control, optimization, estimation, and approximation.
:sound: Collected C++ implementations of the classic 4-pole moog ladder filter
Fast Fourier transforms (FFTs) in Rust
Control adaptive filters with neural networks.
A Machine Learning Approach of Emotional Model
Source code for the book Code Your Own Synth Plug-Ins With C++ and JUCE
Digital signal analysis library for python. The library includes such methods of the signal analysis, signal processing and signal parameter estimation as ARMA-based techniques; subspace-based techniques; matrix-pencil-based methods; singular-spectrum analysis (SSA); dynamic-mode decomposition (DMD); empirical mode decomposition; variational mode decomposition (EMD); empirical wavelet transform (EWT); Hilbert vibration decomposition (HVD) and many others.
《FPGA应用开发和仿真》(机械工业出版社2018年第1版 ISBN:9787111582786)的源码。Source Code of the book FPGA Application Development and Simulation(CHS).
👁️ An authorial set of fundamental Python recipes on Computer Vision and Digital Image Processing.
Implement Digital Signal Processing (DSP) systems and create audio applications using high performance and energy-efficient Arm processors
A spectral visualizer that analyzes the frequencies of music and sound, written in Godot 3.1.
Simulate optical communications systems with Python.
This repo summarizes the courses and materials for speech signal processing. You are kindly invited to pull requests.
A list of open resources for learning and working with digital signal processing.
Understand of the fundamentals of digital signal processing for Machine Learning/Deep Learning applications.
Electrical Engineering Formulas in Python