Andrea Martelloni

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About

Me after climbing most of Akrafjall

Me after climbing most of Akrafjall

I’m a product-focused AI leader who turns research into real products. I work across the full product stack—from building DSP and ML algorithms to shaping product strategy and bringing products to market. As CEO/co-founder of the award-winning HITar spin-out, and over a number of technical jobs in the audio industry, I’ve led ideas from initial concept to launch, delivering products now used by professionals worldwide.

Whether I’m building embedded audio systems, creating machine learning frameworks like MEML, or leading product vision, I bring the same hands-on approach to every level. I thrive at the intersection of deep technical building and product thinking, making sure what we create solves real problems and represents the best value for the customer.

💼 Recent Work History

June 2024
October 2025		 University of Sussex – Brighton, UK
Research Fellow in Musical Instrument Design and Creative Machine Learning
  • Research Musically Embodied Machine Learning, using musical interfaces to train ML in real time on RP2350 and XMOS Xcore.ai platforms
  • Concurrent system-level design (NN + audio/peripheral) on multi-core MCUs
  • Create meMLP, an edge AI library with support for RL and on-device training
September 2023
Present		 Queen Mary University of London – London, UK
Co-founder and CEO, HITar spin-out
  • Winner of two QM Impact Fund Grants (£50k), MIDI Innovation Awards 2023
  • Commercialisation: business model, investor/sales pitching, sales funnel, qualifying leads, patent writing
  • Productisation: quantisation (ONNX), embedded deployment (TFLiteMicro)
September 2017
December 2019		 ams AG (Semiconductors and sensors) – Stokenchurch, UK
Digital Signal Processor Software Engineer
  • Implement adaptive hybrid Active Noise Cancellation (ANC) algorithms.
  • Develop Automatic Leakage Compensation on the proprietary AS3460 chip.
  • Drive product development from research simulation to finished product within one year, collaborating closely with acoustical engineering
March 2015
September 2017		 Blackstar Amplification Ltd – Northampton, UK
DSP Engineer
  • Conduct R&D of non-linear digital audio processing algorithms and effects, from ideation to assembly hand-optimisation
  • Launched products: ID:Core digital amps; Venue MkII hybrid amps; Unity bass amps
October 2013
March 2015		 ITDev Ltd – Southampton, UK
Graduate Software Engineer
  • Software consultancy with PHP + MySQL, Python + SQLite, JS, Perl, embedded C
  • Code quality, development practices: CI/CD, unit testing, serve as SCRUM master
  • Embedded development on video encoder systems

🎓 Education and Qualifications

PhD Artificial Intelligence and Music

Queen Mary University of London, Centre for Digital Music September 2019 – April 2025

MSc Sound and Vibration Studies - Merit

University of Southampton, Institute of Sound and Vibration Research September 2012 – September 2013

BSc Informatica Musicale (Musical Information Technology) - 110/110 cum laude

Università degli Studi di Milano September 2009 – September 2012

🛠️ Technical Skillset

Software concepts, languages:
C++ C Python ARM Assembly
Javascript PHP Perl
CI/CD Unit testing SCRUM

Hardware platforms:
ARM Cortex-M XMOS x86
Xtensa HiFi3 ARM Cortex-A SHARC

AI concepts, toolkits:
Pytorch Torchlib
VAECNNGRU/LSTM
Optimisation ONNX TFLite-micro
  • DSP Concepts: Fixed-point signal chains; LMS; optimised fixed-point STFT; multi-rate processing
  • Audio Effects: Pitch shifting, reverb, fuzz; model analogue gain stages; feedback/feed-forward compressors; complex signal chains on constrained DSPs
  • Toolchains & Testing: Unit testing: microunit, catch2, unity; continuous integration: Jenkins; automated DSP testing with CI pipelines; native C++ ↔ Python integration via Boost::Python, PyBind11
  • ML/AI Concepts: ML from scratch in embedded C++; CNNs, CRNNs, VAEs in embedded real-time AI; reinforcement learning: deep deterministic policy gradient (DDPG); int8/int16 quantisation with ONNX; deployment into VST, Max/MSP, ARM Cortex-M, XMOS
  • Acoustics: FF/FB noise cancellation; room modelling (FDTD, FEM, Digital Waveguide); spatial hearing and psychoacoustics; acoustic simulation: MATLAB, CATT-Acoustic, COMSOL

☀️ Other Interests

🏆 Awards and Funding

Grants

2024, April - 2025, July: Queen Mary Impact Fund Grant. HITar: HyVibe smart guitar integration and user acceptance testing. Co-investigator.
£23,000

2023, September - 2024, December: Queen Mary Impact Fund Grant. HITar: commercialisation and market research. Co-investigator.
£20,000

Awards

2023 MIDI Innovation Awards: Best Hardware Prototype (HITar).

2023 Guthman Musical Instrument Competition: third place (HITar).

📚 Publications

Google Scholar Profile: scholar.google.com/citations?user=sMOTnGcAAAAJ

Thesis

Martelloni, A., 2025. Real-Time AI to Augment Percussive Fingerstyle on the Acoustic Guitar (Doctoral dissertation, Queen Mary University of London). Supervised by Dr Mathieu Barthet and Prof Andrew McPherson.

Conference papers

Martelloni, A., Kiefer, C., 2025, Towards an Ecosystem of Instruments of Tunable Machine Learning, in Proceedings of the 6th Conference on AI Music Creativity (AIMC 2025).

Martelloni, A., McPherson, A. and Barthet, M., 2023, November. Real-Time Percussive Technique Recognition and Embedding Learning for the Acoustic Guitar. In ISMIR 2023.

Reed, C.N., Nordmoen, C., Martelloni, A., Lepri, G., Robson, N., Zayas-Garin, E., Cotton, K. and McPherson, A., 2022, June. Exploring experiences with new musical instruments through micro-phenomenology. In NIME 2022.

Martelloni, A., McPherson, A. and Barthet, M., 2021. Guitar augmentation for Percussive Fingerstyle: Combining self-reflexive practice and user-centred design. In NIME 2021.

Martelloni, A., McPherson, A. and Barthet, M., 2020. Percussive Fingerstyle Guitar through the Lens of NIME: an Interview Study. In NIME 2020.

Martelloni, A., Mauro, D.A. and Mancuso, A., 2013, June. Further evidences of the contribution of the ear canal to directional hearing: design of a compensation filter. In International Conference of Acoustics (ICA) 2013

Patent applications

Martelloni, A., Barthet, M., and McPherson, A., 2024. Apparatus and technique for the rich representation of impacts and dynamic interaction on surfaces through sensors and real-time machine learning. PCT Patent Application No. PCT/GB2024/050042.

📣 Presentations and Workshops

Presentations

Martelloni, A and Kiefer, C., 2024, Musically Embodied Machine Learning, at the workshop on Ethical and Responsible Music Making with AI, July 2024.

Martelloni, A and Caspe, F., 2023, Deep learning for DSP engineers: challenges & tricks for audio AI, Audio Developer Conference 2023.

Organised workshops

Kiefer, C. and Martelloni, A., workshop on Musically Embodied Machine Learning. Sessions at the following venues:

Attended workshops

Armitage, J., Shepardson, V., Privato, N., Pelinski, T., Benito Temprano, A.L., Wolstanholme, L., Martelloni, A., Caspe, F.S., Reed, C.N., Skach, S. and Diaz, R., 2023, August. Agential Instruments Design Workshop. 4th International Conference on AI and Music Creativity (AIMC).

Pelinski, T., Shepardson, V., Symons, S., Caspe, F.S., Temprano, A.L.B., Armitage, J., Kiefer, C., Fiebrink, R., Magnusson, T. and McPherson, A., 2022, June. Embedded ai for nime: Challenges and opportunities. In NIME 2022.

Performances and invited demos

Martelloni, A., 2025. A HITar multimodal performance. For the launch event of the HARMONIE project, 4th March 2025, Marseille, France.

Martelloni, A., 2023. Sliogán: a performance composed for the HITar. Performance for the music track at ISMIR 2023.

Martelloni, A., McPherson, A. and Barthet, M., 2022. Augmented Guitar for Percussive Fingerstyle. Demo at Haptic & Audio Interaction Design conference.