Leading countless AI projects has left me very aware of all the challenges we may encounter during the development process. Therefore, I created a roadmap for AI managers and consultants to follow when creating an AI strategy, so they can better navigate the road to a successful AI strategy. Read the full article here.

I'm excited to announce this internship opportunity with Sounder Music in the Netherlands. This internship in data analytics for music will be (remotely) co-supervised by myself (Prof. Dorien Herremans) and the founder of Sounders Music (Willem Bloem). Send Willem a message if you are interested with subject [Sounders internship] to willem [period] bloem [aat] noticesound.com. Ideally we can come to a research publication at the end of the project.

Two years ago, I attended the Lorenz workshop on Music, Health, and Computing in at the University of Leiden. After a long and thorough process, a roadmap paper was published. All of the workshop attendees, who are experts in either music therapy or music technology, put their heads together, to create this important roadmap for the future of this new interdisciplinary field.

I was excited to give a keynote at DMRN+15. More info on the workshop here. Listen to my keynote below!

Controllable music generation: from MorpheuS to deep networks

Nicolas Guo, Dr. Dimos Markis and myself just published a new paper on Hierarchical Recurrent Neural Networks for Conditional Melody Generation with Long-term Structure. Inspired by methods from the audio domain, such as SampleRNN, we explore how we can generate melodies, conditioned by chords by inputting training data in multiple granularities.

Singa is offering fellowships for International PhD students in Singapore. If you are interested in working in the AMAAI lab, send me a message. I am interested in supervising PhD students interested in the domain of Music Information Retrieval or AI for multimedia or finance.

More details on the application: https://www.a-star.edu.sg/Scholarships/for-graduate-studies/singapore-in...

Deadline for applications: January 1st!

PhD student's Thao Phuong's paper on multimodal emotion prediction from movies/music is now available on Arxiv, together with the code. AttendAffectNet uses transformers with feature-based attention to attend to the most useful features at any given time to predict the valence/arousal.

Ha Thi Phuong Thao, Balamurali B.T., Dorien Herremans, Gemma Roig, 2020. AttendAffectNet: Self-Attention based Networks for Predicting Affective Responses from Movies. arXiv:2010.11188

Preprint paper.

Hao Hao Tan and Jyun Luo will be presenting their work at ISMIR2020 This week!

Tan H.H., Herremans D.. 2020. Music FaderNets: Controllable Music Generation Based On High-Level Features via Low-Level Feature Modelling. Proceedings of the International Society of Music Information Retrieval (ISMIR).. Preprint link.

Congratulations to Raven for publishing 'nnAudio: An on-the-fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolutional Neural Networks', in IEEE Access. nnAudio allows you to calculate spectrograms (linear, log, Mel, CQT) on-the-fly as a layer in PyTorch. This makes the spectrograms finetunable to your task! nnAudio is easy to install with pip, see instructions at https://github.com/KinWaiCheuk/nnAudio