Convolutional-based supervised regression task for extracting high level timbral features from drums sound files, useful to condition a real time Neural Sound Synthesiser on continuous intuitive controls.
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Updated
Aug 1, 2023 - Jupyter Notebook
Convolutional-based supervised regression task for extracting high level timbral features from drums sound files, useful to condition a real time Neural Sound Synthesiser on continuous intuitive controls.
A server to host JAMS audio feature extraction data
High level audio features for Javascript
GTZAN Music genre classification using Logistic regression and SVM.
A simple music feature extractor for Deep Learning models
Audio input -> real-time analysis -> OSC output. Takes in real-time audio, does feature extraction using smart algorithms then sends out OSC to be used in other programs.
Trained a CNN model to classify whale calls into an A-call or not
AudioInspect is an app that extracts audio features from uploaded audio files or audio files in a specified folder, providing insights into the characteristics of the audio.
Generation of music playlists based on audio features analysis using Essentia and the MusAV dataset
An Object Oriented framework for easy feature logging on ChucK systems
Node changed their float implementation and broke Meyda. This was a repro
A library that loads recordbox xml files and classifies other files based on data learned from existing entries based on a neural network.
Haskell and I are giving it another go.
Various Neural Network Architectures for Supervised Tonic classification using the mridangam_stroke dataset, and supervised instrument classification on the TinySOL dataset.
Scratch for experimenting with audio feature extraction.
Python Script to suggest the volume at which the music audio file needs to be played for better experience and feeling.
Developed a deep learning model using Multi-Layer Perceptron to recognize and classify speech signals into 6 distinct emotions. Extracted 160 audio features, enabling the model to detect emotions with around 75% accuracy on the training set. Implemented the model on a Streamlit dashboard.
Text-independent speaker identification system based on GMM
Tooling and datasets for neural-network powered audio feature based synthesis
Java Implementation of the Sonopy Audio Feature Extraction Library by MycroftAI
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