melspectrogram

kapre: Keras Audio Preprocessors

Audio processing by using pytorch 1D convolution network

UrbanSound classification using Convolutional Recurrent Networks in PyTorch

⭐ 本科毕业设计：基于内容的音乐推荐系统设计与开发。使用了Pytorch框架构建训练模型代码，使用Django构建了前后端。

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.

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

Learnable STRF, from Riad et al. 2021 JASA

A packaged convolutional voice activity detector for noisy environments.

musical genres binary classification using pytorch.audio and keras

Perform three types of feature extraction: STFT, MFCC and MelSpectrogram. Apply CNN/VGG with or without RNN architecture. Able to achieve 95% accuracy.

A C++ implementation of stft, melspectrogram and mel_to_stft

Fashion Mnist and "recognize a speaker" datasets were utilized for image classification. For this classification task were tried to apply transfer learning from Mnist Fashion to "Recognize a Speaker" and transfer learning inside of Mnist Fashion.

A simple Speaker classifier using Keras

audio classification fastai - Convert audio files into images for classification

In this project, I implemented Convolutional Neural Networks on images of melspectrogram of sound files.

CNN-LSTM model for audio emotion detection in children with adverse childhood events.

An example repository to analyze cough audio data using transfer learning

Signal Processing with Python and Librosa

Classifying Music Genre with Urban Sound Dataset, Preprocessing with Librosa and Torch audio, Model made in Tensorflow and PyTorch

Detecting emotions from audios using neural networks