Category Research

Congratulations to two AIM PhD students, Andrea Martelloni and Max Graf, who have both won MIDI Innovation Awards 2023 in the hardware prototype and software prototype categories, respectively.

Two AIM PhD students have each won an award at this year’s MIDI Innovation Awards. Now in its 40th year, the MIDI Awards showcase products and projects that are using MIDI (Musical Instrument Digital Interface) in fresh and original ways, highlighting the role that MIDI technology has to play in enabling musical creativity.

Andrea Martelloni won the hardware prototype category for the HITar, an augmented percussive guitar which Andrea developed under the supervision of Dr Mathieu Barthet and Professor Andrew McPherson. Aimed at percussive fingerstyle guitarists, the HITar is a device that can be fitted to a regular acoustic guitar and can alter the way a player interacts with the instrument’s body. The unit employs sensors placed underneath the areas most commonly struck by players and uses an AI engine to determine which part of the hard is used for each percussive hit. The resulting MIDI output can then be used to integrate hardware or software instruments or samples into a performance, allowing guitarists to trigger drum and percussion samples or blend sample libraries and virtual instruments with their playing.

In the software category, Max Graf won for Netz, a mixed reality (MR) software instrument that blends the real and virtual worlds by displaying 3D virtual objects within a real environment. It’s a self-contained instrument that features an embedded sound engine, allowing users to produce sounds directly from the head-mounted display. The software’s interface appears as a network where nodes represent notes, which can be mapped to a tangible surface for tactile feedback or be positioned in the air. Performers’ hand poses and gestures are tracked in in real time, allowing Netz to translate subtle hand movements to expressive musical controls; gestures such as the opening and closing of fingers — or movements such as wrist rotation — are recognised and interpreted by the system and can be assigned to specific instrument parameters and MIDI controls.

Many congratulations from everyone in AIM to Andrea, Max and their supervisors for this huge achievement!

More information:

• MIDI Innovation Awards Sound on Sound – MIDI Innovation Awards 2023: Winners announced
• HITar Twitter/X and Instagram page

On 22-25 October, several AIM researchers will participate at the 2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, taking place at the Mohonk Mountain House in New Paltz, NY, USA. WASPAA is a premier event in the field of audio signal processing, organised by the IEEE’s Audio and Acoustic Signal Processing (AASP) technical committee, with a strong focus on music signal processing and computational sound scene analysis.

The AI and Music Centre for Doctoral Training, as in previous years, will have a strong presence at WASPAA 2023.

In the Technical Programme, the following papers are authored by AIM members:

• Perceptual Musical Similarity Metric Learning with Graph Neural Networks (Cyrus Vahidi, Shubhr Singh, George Fazekas, Emmanouil Benetos, Dan Stowell, Huy Phan, Mathieu Lagrange) Postprint
• Leveraging synthetic data for improving chamber ensemble separation (Saurjya Sarkar, Louise Thorpe, Emmanouil Benetos, Mark B Sandler) Postprint
• General Purpose Audio Effect Removal (Matthew Rice, Christian J. Steinmetz, George Fazekas, Joshua D. Reiss) Postprint

In the Demo Session, the following demos will be presented by AIM members:

• Music Ensemble Separation using Permutation Invariant Training (Saurjya Sarkar, Emmanouil Benetos, Mark B Sandler)

See you at WASPAA!

Sequences in London is a 2-day workshop that aims to gather researchers working on different aspects of symbolic sequences, including string algorithms and data structures, computational biology, music information retrieval, and combinatorics on words, as well as application areas.

At the half-day for music information retrieval, AIM student Lele Liu gave a talk about her work on sequence modelling for audio-to-score music transcription. The talk briefly introduces her research in transcribing musical recordings into machine-readable scores by using deep sequential models. Feel free to find out more about her research here!