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Search results for mel spectrogram

mel-spectrogram x

25 search results found

Nnaudio ⭐ 882

Audio processing by using pytorch 1D convolution network

kapre: Keras Audio Preprocessors

Crnn Audio Classification ⭐ 249

UrbanSound classification using Convolutional Recurrent Networks in PyTorch

Tts Cube ⭐ 216

End-2-end speech synthesis with recurrent neural networks

Neural Voice Cloning With Few Samples ⭐ 211

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

Polyphonicpianotranscription ⭐ 106

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

Speech Emotion Classification With Pytorch ⭐ 100

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

Realbook ⭐ 95

Easier audio-based machine learning with TensorFlow.

A simple audio feature extraction library

Audio_classification ⭐ 69

CNN 1D vs 2D audio classification

Deep Music Tagger ⭐ 41

Music genre classification model using CRNN

Torch Mfcc ⭐ 37

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

Speech Emotion Webapp ⭐ 33

Speech Emotion Recognition

Zaf Matlab ⭐ 27

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.

Urban Sound Classification ⭐ 19

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

Wavenet Like Vocoder ⭐ 17

Basic wavenet and fftnet vocoder model.

Melspectrogram_cpp ⭐ 12

C/C++实现Python音频处理库librosa中melspectrogram的计算过程

Zaf Python ⭐ 11

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.

Deepmultispeech ⭐ 10

Deep Multi-Speech model

Lpvspectral.jl ⭐ 10

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

Polish bird species recognition - Bird song analysis and classification with MFCC and CNNs. Trained on EfficientNets with final score 0.88 AUC. Women in Machine Learning & Data Science project.

A packaged convolutional voice activity detector for noisy environments.

Lpc_for_tts ⭐ 7

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

Image Classification Lab ⭐ 7

musical genres binary classification using pytorch.audio and keras

Piano Classification ⭐ 5

This study converts piano recordings to mel spectrogram and classifies them by SOTA pre-trained neural network backbones in CV. Comparative experiments show that SqueezeNet achieves a best classification accuracy of 92.37%.|该项目将钢琴录音转为为mel频谱图，使用微调后的前沿计算机视觉领域预训练深度学习骨干

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