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ML4A / Soundcube

ML4A / Soundcube

Soundcube
https://github.com/opendot/ml4a-soundcube

This project has been developed during the one-week workshop "Machine Learning for artists" held by Gene Kogan in Opendot on November 2016.

/ Concept
The project has two main objectives: the first is to use convolutional neural networks to classify audio samples according to a predefined set of categories, with a focus on environmental sounds. The second objective is to build a 3D immersive virtual environment where sounds are placed based on their similarity, allowing users to explore them and play them as he "walks" inside the space. The first part of the project could be useful for sound designers: they could build their own dataset and obtain an automatic classification of new sounds they want to use. It could also be useful to listen to the environment and detect special events happening in the "soundscape" surrounding the user. The second part of the project, on the other hand, can be especially useful for exhibitions, or for music performances. The way in which the 3d environment can be explored are many: for example, a composer could define a logic to walk inside the 3D space in a semi automated way, or devices like a Kinect or a Leap Motion could be used to move the focus on a particular zone in the environment.

/ Realisation
We based our implementation on a paper by Karol J. Piczak, describing a suitable approach for what we wanted to do. A dataset of approximately 4000 sounds divided in 4 categories (wood, water, fans and voices) was built in order to have a training set for the machine learning step. The idea is to use the last layer of the convnet as feature vector for each sound, and to use t-SNE in order to reduce the dimensions of the feature vector to 3 and be able to plot each audio sample on our 3d environment.

/ Final result
While the sound classification is still a work in progress, we managed to build the 3d environment using OpenFrameworks and using t-SNE directly with some spectral features describing each audio chunk. The current ways to exlore it are either by using a mouse and hovering on the spheres to listen to the associated sound, or to send a set of X,Y and Z coordinates via OSC to position the listener in the space: the 6 nearest sounds will be heard according to their position, using ambisonics.

/ Team
Gabriele Balzano, Francesca Bonalume, Daniele Ciminieri, Gene Kogan, Jacopo Foglietti, Alessandro Inguglia, Sergio Missaglia, Luca Mucci, Massimiliano Viel, Max Zanoni and Opendot