Deep Learning for Multimodal Audio Intelligence with Emotional Insight

A deep learning system that understands audio from several modalities and reads emotional insight from it.

A deep learning system that understands audio from several modalities and reads emotional insight from it. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want to work with signals and wireless data: acquiring or simulating signals, filtering and extracting features, and evaluating the results.

The main parts are fusion of more than one type of input, signal acquisition or simulation, filtering and feature extraction, a processing or learning model, and plots and a clear evaluation. The reference build uses TensorFlow, Python, NumPy, SciPy, and MATLAB, with a clear project structure, setup instructions and sample data so that it can be run and extended.