kapre: Keras Audio Preprocessors
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Updated
Oct 23, 2023 - Python
kapre: Keras Audio Preprocessors
Audio processing by using pytorch 1D convolution network
UrbanSound classification using Convolutional Recurrent Networks in PyTorch
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.
⭐ 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。
Perform three types of feature extraction: STFT, MFCC and MelSpectrogram. Apply CNN/VGG with or without RNN architecture. Able to achieve 95% accuracy.
Music recommendation using neural network
Signal Processing with Python and Librosa
Learnable STRF, from Riad et al. 2021 JASA
CNN-LSTM model for audio emotion detection in children with adverse childhood events.
A packaged convolutional voice activity detector for noisy environments.
A simple Speaker classifier using Keras
Detecting emotions from audios using neural networks
musical genres binary classification using pytorch.audio and keras
In this project, I implemented Convolutional Neural Networks on images of melspectrogram of sound files.
There are already many projects underway to extensively monitor birds by continuously recording natural soundscapes over long periods. However, as many living and nonliving things make noise, the analysis of these datasets is often done manually by domain experts. These analyses are painstakingly slow, and results are often incomplete.
Given mel-frequency spectrogram predict whether audio is clean or noisy
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.
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.
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