High level audio features for Javascript
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Updated
Feb 26, 2016
High level audio features for Javascript
Tooling and datasets for neural-network powered audio feature based synthesis
Haskell and I are giving it another go.
A server to host JAMS audio feature extraction data
Node changed their float implementation and broke Meyda. This was a repro
Speaker recognition using Mel Frequency Cepstral Coefficients (MFCC) and Linde-Buzo-Gray (LBG) clustering algorithm
Java Implementation of the Sonopy Audio Feature Extraction Library by MycroftAI
A simple music feature extractor for Deep Learning models
Text-independent speaker identification system based on GMM
GTZAN Music genre classification using Logistic regression and SVM.
Drum Samples Clustering, Audio feature extraction and clustering audio files using data visualization and dimensionality reduction (PCA).
An Object Oriented framework for easy feature logging on ChucK systems
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
Generation of music playlists based on audio features analysis using Essentia and the MusAV dataset
Scratch for experimenting with audio feature extraction.
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.
Urban Sound Annotation and Classification
Various Neural Network Architectures for Supervised Tonic classification using the mridangam_stroke dataset, and supervised instrument classification on the TinySOL dataset.
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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