spectrogram

GUI for a Vocal Remover that uses Deep Neural Networks.

kapre: Keras Audio Preprocessors

Audio processing by using pytorch 1D convolution network

Real-time audio visualizations (spectrum, spectrogram, etc.)

Music recommender using deep learning with Keras and TensorFlow

Vocal Remover using Deep Neural Networks

🎶 Real-time audio spectrogram generator for the web

UrbanSound classification using Convolutional Recurrent Networks in PyTorch

Predicting depression from acoustic features of speech using a Convolutional Neural Network.

Encode an image to sound and view it as a spectrogram - turn your images into music

Turn an image into sound whose spectrogram looks like the image.

Audio spectrogram in canvas.

.NET library for creating spectrograms (visual representations of frequency spectrum over time)

A .NET Standard library for computing the Fast Fourier Transform (FFT) of real or complex data

Real time monaural source separation base on fully convolutional neural network operates on Time-frequency domain.

mpv lua scripts

This repository contains PyTorch implementation of 4 different models for classification of emotions of the speech.

Real-time 2D spectrogram plugin (LV2, VST and Jack)

Frequency domain estimation and functional and directed connectivity analysis tools for electrophysiological data

Source code complementing our paper for acoustic event classification using convolutional neural networks.

Final Year Project for Speech Separation

Sound-based Bird Classification - using AI, acoustics and ornithology to classify birds in the environment, an environmental awareness project (Web Application, Flask, Python)

Audio Classification using Image Classification

pytorch tacotron2 https://arxiv.org/pdf/1712.05884.pdf

The 2018 LifeCLEF bird identification task baseline system.

streamline acoustic analysis in R

Source code of the TUCMI submission to BirdCLEF2017

Recommending Music using a Convolutional Neural Network.

c++ implementation of the fingerprinting algorithm suggested in the dejavu audio fingerprinting project