3 research outputs found
AI (r)evolution -- where are we heading? Thoughts about the future of music and sound technologies in the era of deep learning
Artificial Intelligence (AI) technologies such as deep learning are evolving
very quickly bringing many changes to our everyday lives. To explore the future
impact and potential of AI in the field of music and sound technologies a
doctoral day was held between Queen Mary University of London (QMUL, UK) and
Sciences et Technologies de la Musique et du Son (STMS, France). Prompt
questions about current trends in AI and music were generated by academics from
QMUL and STMS. Students from the two institutions then debated these questions.
This report presents a summary of the student debates on the topics of: Data,
Impact, and the Environment; Responsible Innovation and Creative Practice;
Creativity and Bias; and From Tools to the Singularity. The students represent
the future generation of AI and music researchers. The academics represent the
incumbent establishment. The student debates reported here capture visions,
dreams, concerns, uncertainties, and contentious issues for the future of AI
and music as the establishment is rightfully challenged by the next generation
Real-time drum accompaniment using transformer architecture
Comunicació presentada a AIMC 2022, celebrada del 13 al 15 de setembre de 2022 en línia.This paper presents a real-time drum generation system capable of accompanying
a human instrumentalist. The drum generation model is a transformer encoder
trained to predict a short drum pattern given a reduced rhythmic representation. We
demonstrate that with certain design considerations, the short drum pattern generator can be used as a real-time accompaniment in musical sessions lasting much
longer than the duration of the training samples. A discussion on the potentials,
limitations and possible future continuations of this work is provided
Completing audio drum loops with symbolic drum suggestions
This work has been presented at NIME'23, at Mexico City, Mexico. 31 May - 2 June, 2023.Sampled drums can be used as an affordable way of creating
human-like drum tracks, or perhaps more interestingly,
can be used as a mean of experimentation with rhythm
and groove. Similarly, AI-based drum generation tools can
focus on creating human-like drum patterns, or alternatively,
focus on providing producers/musicians with means
of experimentation with rhythm. In this work, we aimed
to explore the latter approach. To this end, we present a
suite of Transformer-based models aimed at completing audio
drum loops with stylistically consistent symbolic drum
events. Our proposed models rely on a reduced spectral
representation of the drum loop, striking a balance between
a raw audio recording and an exact symbolic transcription.
Using a number of objective evaluations, we explore the validity
of our approach and identify several challenges that
need to be further studied in future iterations of this work.
Lastly, we provide a real-time VST plugin that allows musicians/
producers to utilize the models in real-time production
settings