Onsets and Velocities: Affordable Real-Time Piano Transcription Using Convolutional Neural Networks

Polyphonic Piano Transcription has recently experienced substantial progress driven by the application of sophisticated Deep Learning setups and the introduction of new subtasks such as note onset, offset, velocity and pedal detection. In this work, we focus on onset and velocity detection, presenting a convolutional neural network with substantially reduced size (3.1M parameters) and a simple inference scheme that achieves state-of-the-art performance on the MAESTRO dataset for onset detection (F1=96.78%) and sets a good novel baseline for onset+velocity (F1=94.50%), while maintaining real-time capabilities on modest commodity hardware. Furthermore, our proposed ONSETS&VELOCITIES (O&V) model shows that a time resolution of 24ms is competitive, countering recent trends. We provide open-source software to reproduce our results and a real-time demo with a pretrained model.