There are 2 repositories under stft topic.
Pyroomacoustics is a package for audio signal processing for indoor applications. It was developed as a fast prototyping platform for beamforming algorithms in indoor scenarios.
Audio processing by using pytorch 1D convolution network
Synchrosqueezing, wavelet transforms, and time-frequency analysis in Python
YASA (Yet Another Spindle Algorithm): a Python package to analyze polysomnographic sleep recordings.
Real time monaural source separation base on fully convolutional neural network operates on Time-frequency domain.
Audio Fingerprinting & Retrieval for .NET
STFT based real-time pitch and timbre shifting in C++ and Python
Simple demo of filtering signal with an LPF and plotting its Short-Time Fourier Transform (STFT) and Laplace transform, in Python.
C library for audio noise reduction and other spectral effects
Zafar's Audio Functions in Python for audio signal analysis: STFT, inverse STFT, mel filterbank, mel spectrogram, MFCC, CQT kernel, CQT spectrogram, CQT chromagram, DCT, DST, MDCT, inverse MDCT.
Zafar's Audio Functions in Matlab for audio signal analysis: STFT, inverse STFT, mel filterbank, mel spectrogram, MFCC, CQT kernel, CQT spectrogram, CQT chromagram, DCT, DST, MDCT, inverse MDCT.
Time stretching audio without changing pitch
Visualization of Audio Spectrogram with Short-Time Fourier Transform (STFT)
Noise-level estimation using minima controlled recursive averaging approach and denoising using Stein's unbiased risk estimates in STFT domain.
Doppler radar signal processing tested with HB100 and RCWL0516 radar modules
A simple tutorial of wavelet, STFT and FFT
Speech Denoising using RNNs in Tensorflow
Official JUCE plugin for stftPitchShift
CRED: A deep residual network of convolutional and recurrent units for earthquake signal detection
MATLAB + Python implementations of real-time median-filtering Harmonic-Percussive Source Separation
This repository contains the trained deep learning models for the detection and prediction of Epileptic seizures.
A Python implementation of STFT and MFCC audio features from scratch
Short-time Fourier transform (STFT) for JAX
Perform three types of feature extraction: STFT, MFCC and MelSpectrogram. Apply CNN/VGG with or without RNN architecture. Able to achieve 95% accuracy.
Spectral RNNs with adaptive window learning in TensorFlow, ICANN 2020.