Audio processing by using pytorch 1D convolution network

kapre: Keras Audio Preprocessors

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

A packaged convolutional voice activity detector for noisy environments.

Learnable STRF, from Riad et al. 2021 JASA

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

musical genres binary classification using pytorch.audio and keras

Signal Processing with Python and Librosa

A simple Speaker classifier using Keras

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.

An example repository to analyze cough audio data using transfer learning

audio classification fastai - Convert audio files into images for classification

During the project for the DIGITAL SIGNAL IMAGE MANAGEMENT course I learned how to manage and process audio and image files. The aim of the project was the classification, through machine learning and deep learning models, of musical genres by extracting specific audio features from the "gtzan dataset" dataset files with which to train the model…

This repository presents the results of a technological initiation that encouraged my undergraduate thesis, I sought to improve my knowledge in digital signal processing applied to music. It is a spectral analysis tool for the audio of musical instruments, focusing on objectively characterizing timbre.

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

The study presents a method to detect emotional states in real-time using audio data. It preprocesses the audio and trains a CNN to recognize emotions, achieving high accuracy. The system has potential applications in interactive systems and mental health monitoring, contributing to the development of emotionally intelligent technology.