A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learning.
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Updated
May 3, 2024 - Python
A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learning.
Fast audio data augmentation in PyTorch. Inspired by audiomentations. Useful for deep learning.
Soundscape analysis with BirdNET.
Removing background noise in a sound file
Turn an image into sound whose spectrogram looks like the image.
Experimental code: sound file preprocessing to optimize Whisper transcriptions without hallucinated texts
📣 Python library for audio augmentation
A video analysis tool built completely in python.
PyTorch implementation of our graph convolutional network (GCN) for human motion generation from music. Also with paired dance-music data for training!
Pitch-shift audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.
A simple yet effective Audio-to-Midi Automatic Piano Transcription system
Soundstorm is a cutting-edge AI-powered audio manipulation application designed to provide a rich yet simplified experience for sound designers, algorithmic composers, and experimental audio enthusiasts. From sample pack creation and algorithmic composition to AI text-to-audio and onscreen ChatGPT, Soundstorm is a sonic powerhouse.
Sonification tool for turning scatter plots into perceptually uniform sound files for science and science access.
DNN-based hearing aid for real-time sound processing
This project use PANNs for audio tagging and sound event detection, and finally get audio embeddings. Then Milvus is used to search the similarity audio items.
🔊 Study about audio features extraction (repo in french).
Time-stretch audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.
Password Based Audio Steganography to Hide Any Data File in a Wav Audio File
Using a raspberry pi, we listen to the coffee machine and count the number of coffee consumption
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