PhD ThesisIn this thesis we propose a bottom-up approach for modelling instrumental gestures and techniques, using piano pedalling as a case study. Pedalling gestures play a vital role in expressive piano performance. They can be categorised into di erent pedalling techniques. We propose several methods for the indirect acquisition of sustain-pedal techniques using audio signal analyses, complemented by the direct measurement of gestures with sensors. A novel measurement system is rst developed to synchronously collect pedalling gestures and piano sound. Recognition of pedalling techniques starts by using the gesture data. This yields high accuracy and facilitates the construction of a ground truth dataset for evaluating the audio-based pedalling detection algorithms. Studies in the audio domain rely on the knowledge of piano acoustics and physics. New audio features are designed through the analysis of isolated notes with di erent pedal e ects. The features associated with a measure of sympathetic resonance are used together with a machine learning classi er to detect the presence of legato-pedal onset in the recordings from a speci c piano. To generalise the detection, deep learning methods are proposed and investigated. Deep Neural Networks are trained using a large synthesised dataset obtained through a physical-modelling synthesiser for feature learning. Trained models serve as feature extractors for frame-wise sustain-pedal detection from acoustic piano recordings in a proposed transfer learning framework. Overall, this thesis demonstrates that recognising sustain-pedal techniques is possible to a high degree of accuracy using sensors and also from audio recordings alone. As the rst study that undertakes pedalling technique detection in real-world piano performance, it complements piano transcription methods. Moreover, the underlying relations between pedalling gestures, piano acoustics and audio features are identi ed. The varying e ectiveness of the presented features and models can also be explained by di erences in pedal use between composers and musical eras
