PhD Thesis

My PhD thesis concerned separating the audio corresponding to the instruments in an orchestral music mixture. This allows for interesting applications such as re-creating the experience of the concert in virtual reality applications.

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Research projects

My research interests span a diverse range of topics: machine learning, signal processing, fairness and explainability of machine learning models, music information retrieval. I participated in interdisciplinary projects, such as HUMAINT, Shake-it and PHENICX.

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Code & data

Check out my latest deep learning repository in python. I am committed to the principles of research reproducibility. Most of the code is made available through github, along with links to the dataset and instructions on how to replicate experiments.

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New Blog Articles

High frequency magnitude spectrogram reconstruction for music mixtures using convolutional autoencoders

Music Bandwidth Expansion

autoencoders in frequency domain yet again ... Read More ›

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