Domenico Stefani

University of Trento | UNITN · Department of Information Engineering and Computer Science

Recent advancements in deep learning have shown great potential for audio applications, improving the accuracy of previous solutions for tasks such as music transcription, beat detection, and real-time audio processing. In addition, the availability of increasingly powerful embedded computers has led many deep learning framework developers to devis...

Real-time applications of Music Information Retrieval (MIR) have been gaining interest as of recently. However, as deep learning becomes more and more ubiquitous for music analysis tasks, several challenges and limitations need to be overcome to deliver accurate and quick real-time MIR systems. In addition, modern embedded computers offer great pot...

In real-time Music Information Retrieval (MIR), small analysis windows are essential for achieving low retrieval latency. In turn, event-based real-time MIR methods require precise onset detectors to correctly align with the beginning of events such as musical notes. Detectors are typically trained using ground-truth annotations from datasets of in...

Extended playing techniques are a crucial characteristic of contemporary double bass practice. Players find their voice by developing a personal vocabulary of techniques through practice and experimentation. These player-idiosyncratic techniques are used in composition, performance, and improvisation. Today's AI methods offer the opportunity to rec...

Recent years have witnessed significant advancements in deep learning architectures for music, along with the availability of more powerful embedded computing platforms specific to low-latency audio processing tasks. These recent developments have opened promising avenues for new Smart Musical Instruments and audio devices that rely on the executio...

Real-time applications of Music Information Retrieval (MIR) have been gaining interest as of recently. However, as deep learning becomes more and more ubiquitous for music analysis tasks, several challenges and limitations need to be overcome to deliver accurate and quick real-time MIR systems. In addition, modern embedded computers offer great pot...

Recent advancements in deep learning have shown great potential for audio applications, improving the accuracy of previous solutions for tasks such as music transcription, beat detection, and real-time audio processing. In addition, the availability of increasingly powerful embedded computers has led many deep learning framework developers to devis...

Recent advancements in deep learning and embedded platforms for real-time audio processing have made it possible to consider embedding advanced algorithms into musical instruments. Among the many possible applications of such systems, this offers the opportunity to further develop the 45-years-old concept of guitar synthesizer: a real guitar, paire...

Onset detectors are used to recognize the beginning of musical events in audio signals. Manual parameter tuning for onset detectors is a time consuming task, while existing automated approaches often maximize only a single performance metric. These automated approaches cannot be used to optimize detector algorithms for complex scenarios, such as re...

This demo presents the timbreID-VST plugin, an audio plugin in Virtual Studio Technology format dedicated to the embedded real-time classification of individual musical instruments timbres. The plugin was created by porting the code of the timbreID library, a collection of objects for the real-time programming language Pure Data that allows the rea...