Tomas Nihlén is a Swedish creative technologist with a passion for audio/music in combination with machine listening and machine learning.

He has a wide background in trend analysis, tech, communication and innovation. He has studied ICT, marketing and communication including completing a Masters’s Degree in Information Systems from Linköping University and having studied project management for 2 years at IHM Business School in Stockholm. 

He lived in Barcelona and later outside of Girona from 2018 to 2022 where he got to know a lot of creative people. In 2021 he gathered a large multi-disciplinary team called AIMCAT to compete in the 2021 edition of the AI Song Contest. The project attained a lot of local interest including blogs, social media and the local broadcaster TV3 and also Swedish Radio. Their entry won the public vote.

In 2022 he became an external partner to the Barcelona-based creative AI organisation ARTIFICIA helping them expand in the Nordic region.

Right now he is particularly interested in connecting machine listening to data visualisation. This could be used both for creating visuals of audio/music for aesthetic or educational purposes and also to get a deeper understanding of the audio/music being analysed.

Another big interest is trying to use these tools to increase general awareness of the great challenges facing our society.


I use AI for media production, analysis and creative exploration.



Using data and machine learning to new create sounds, instruments, audio and music.

Machine learning can be used in so many ways both, when it comes to the actual sounds and the ways we interact with them. 


Using machine listening to extract properties like pitch, loudness and timbre etc from audio content and music. To evaluate, categorise and understand audio material.

Metadata extracted from audio material can then be used to visualise the audio either for educational or aesthetic purposes.


Machine learning and machine listening can be used to explore new creative possibilities with music or audio material. This exploration can sometimes be done in real-time.