mel-spectrogram

Implementation of Neural Voice Cloning with Few Samples Research Paper by Baidu

End-2-end speech synthesis with recurrent neural networks

Recurrent Neural Network for generating piano MIDI-files from audio (MP3, WAV, etc.)

CNN 1D vs 2D audio classification

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

A simple audio feature extraction library

Linear Prediction Coefficients estimation from mel-spectrogram implemented in Python based on Levinson-Durbin algorithm.

Urban sound source tagging from an aggregation of four second noisy audio clips via 1D and 2D CNN (Xception)

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.

A librosa STFT/Fbank/mfcc feature extration written up in PyTorch using 1D Convolutions.

Basic wavenet and fftnet vocoder model.

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.

Framework for one-shot multispeaker system based on Deep Learning

Open Source Implementation of Neural Voice Cloning with Few Audio Samples (Baidu Research)

Deep Multi-Speech model

Least-squares (sparse) spectral estimation and (sparse) LPV spectral decomposition.

Cough detection with Log Mel Spectrogram, Wavelet Transform, Deep learning and Transfer learning techniques

Convert audio file to melgram (that is, mel-spectrogram) in .NET

Java Implementation of the Sonopy Audio Feature Extraction Library by MycroftAI

A tensorflow application of CNN based music genre classifier which classifies an audio clip based on it's Mel Spectrogram and a RestAPI for inference using tensorflow serving

My best submission to this Kaggle contest

Master's Thesis: Automatic Tagging of Musical Compositions Using Machine Learning Methods

Zafar's Audio Functions in Julia for audio signal analysis: STFT, inverse STFT, CQT kernel, CQT spectrogram, CQT chromagram, MFCC, DCT, DST, MDCT, inverse MDCT.

Music Pace Compatibility Project

Code for "Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features" arXiv:2110.08862, 2021.

Speech emotion recognition models for the Moody web application.

Bali has a diversity of arts that has been recognized by the world, where one of the most famous Balinese arts is the Karawitan art, especially the Kendang Tunggal instrument. Notation documentation or more commonly known as music transcription, can make learning a song easier, and in the case of this research, it makes it easier to learn to play the Kendang Tunggal instrument. The first approach method used to document a kendang tunggal song is onset detection. Onset is when the signal experiences an attack period, which helps segment the sound color of the drum instrument. The segmented kendang tunggal sound color classification uses the Backpropagation algorithm with several features of the frequency domain and time domain as a characteristic of the sound color. Then the kendang tunggal song is revived into a synthetic sound with the Mel Spectral Approximation filter. Based on the research, the optimal parameter for drum sound color segmentation with onset detection is the hop size 110 with normalization of the features on its onset detection function. The optimal backpropagation architecture obtained with a learning rate of 0.9, neurons 10, and epoch 2000 produces an accuracy of 60.85%. The synthesis method using the Mel Log Spectrum Approximation can make synthetic sounds similar to kendang songs with an accuracy of 83.33%

[Big Data] Detection of Animal Species in Tropical Soundscapes

Attention-based Hybrid CNN-LSTM and Spectral Data Augmentation for COVID-19 Diagnosis from Cough Sound

Speech Emotion Recognition using Deep Learning