Optimal transport has recently been reintroduced to the machine learning community thanks in part to novel efficient optimization procedures allowing for medium to large scale applications. We propose a Python toolbox that implements several key optimal transport ideas for the machine learning community. The toolbox contains implementations of a number of founding works of OT for machine learning such as Sinkhorn algorithm and Wasserstein barycenters, but also provides generic solvers that can be used for conducting novel fundamental research. This toolbox, named POT for Python Optimal Transport, is open source with an MIT license.

Music transcription refers to extraction of a human readable and interpretable description from a recording of a music performance. Automatic music transcription remains, nowadays, a challenging research problem when dealing with polyphonic sounds or when removing certain constraints. Some instruments like guitars and violins add ambiguity to the problem as the same note can be played at different positions. When dealing with guitar music tablature are, often, preferred to the usual music score, as they present information in a more accessible way. Here, we address this issue with a system which uses the visual modality to support traditional audio transcription techniques. The system is composed of four modules which have been implemented and evaluated: a system which tracks the position of the fretboard on a video stream, a system which automatically detects the position of the guitar on the first fret to initialize the first system, a system which detects the position of the hand on the guitar, and finally a system which fuses the visual and audio information to extract a tablature. Results show that this kind of multimodal approach can easily disambiguate 89% of notes in a deterministic way.