Congratulations to Thao on leading the publication of the EmoMV dataset set for music-video matching based on emotion!
Pham Q-H, Herremans D., Roig G.. 2022. EmoMV: Affective Music-Video Correspondence Learning Datasets for Classification and Retrieval. Information Fusion. DOI: 10.1016/j.inffus.2022.10.002
Paper highlights:
I was honoured to give a keynote talk at the 3rd Conference on AI Music Creativity (AIMC) on controllable music generation with emotion. Watch the full keynote here:
Congrats on my former research assistant Jiahui Huang on his latest paper in Sensors on 'Single Image Video Prediction with Auto-Regressive GANs'. Now we can generate videos of faces with desired emotions!
Huang, Jiahui, Yew Ken Chia, Samson Yu, Kevin Yee, Dennis Küster, Eva G. Krumhuber, Dorien Herremans, and Gemma Roig. "Single Image Video Prediction with Auto-Regressive GANs." Sensors 22, no. 9 (2022): 3533.
Read my PhD student Yanzhao Zou and myself's latest paper on A Multimodal Model with Twitter Finbert Embeddings for Extreme Price Movement Prediction of Bitcoin.
SUTD Game Lab is looking for a passionate game developer to join our team. The Game Programmer will visualise how systems should work, and translate this into a functioning solution using the existing game engine to develop prototypes or a polished game for training purposes.
Responsibilities:
Read the full NRF magazine here. Our original paper is available here.
It was an honour today to be part of the seminar at the KTH Royal Institute of Technology in Stockholm as part of the dialogues series.
dialogues1: probing the future of creative technology
Subject: “Interaction with generative music frameworks”
Guests: Dorien Herremans and Kıvanç Tatar (Video link to be posted)
Dorien Herremans: Controllable deep music generation with emotion
Excited to be featured on the latest 'AI and You - What is AI? How will it affect your life, your work, and your world?' podcast by Peter Scott from Human Cusp.
We're focusing on AI in music: What's the state of the art in AI music composition, how can human composers use it to their advantage, and what is the AI Song Contest? How do musical AIs surprise their creators and how are they like your grandmother trying to explain death metal?
Last year I was honoured to be part of the panel discussion on 'Challenging the limits of AI for the next generation of co-creative tools - Frontiers of Music and Artificial Intelligence'. at Ars Electronica, IRCAM (FR). Watch the video below.
Congrats to Kin Wa Cheuk for his published paper in the ACM Multimedia conference (A*) on 'ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data'. If you are interested in training low-data music transcription models with semi-supervised learning, check out the full paper here, or access the preprint.
Watch Raven's talk here:
Sounder Music in the Netherlands (https://soundersmusic.com/) has an internship opportunity for a MSc or PhD student in data analytics for music. The internship will be (remotely) co-supervised by myself (Prof. Dorien Herremans, SUTD) and the founder of Sounders Music (Willem Bloem).
Over the last few years, we developed Project PEAR at SUTD Game Lab. Project PEAR is a geolocation based augmented reality game that is aimed at educating the player on climate change as well as influence their behaviours. We just published a study in Sustainability on the effectiveness of this game.
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. The aiSTROM roadmap was just published in IEEE AccessRead the full article here.
Our team at Singapore University of Technology and Design (SUTD) is looking for an RA or postdoc in music and AI. You will be joining our AMAAI Lab in music/audio/vision AI supervised by Prof. Dorien Herremans. At our lab, we aim to advance the state-of-the-art in AI for music and audio. More information on the music/audio team here. We have multiple research lines going that need your expertise, either in symbolic music (midi) as well as audio.
I'm excited to share our latest paper on cough sound classification in Sensors. Full paper available here.
I'm quite excited to announce the book chapter I wrote with Emeritus Prof. P. Kroonenberg from the University of Leiden. Prof. Kroonenberg just published an amazingly meticulous and interesting book on Multivariate Humanities, and I was happy to collaborate with him on the chapter "Musical stylometry: Characterisation of music" (pp. 347-370).
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.
I recently had the pleasure to be featured in a Meet My Lab on "Mathematics & Music - A Synergetic Duo for Automatic Music Generation", together with Prof. Elaine Chew. These sessions are organized by Euraxess, European Commission. [scroll down for video]
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'm very happy to announce that our lab had three papers accepted at the International Joint Conference on Neural Networks (IJCNN) on the topics of controllable music generation with emotion and structure, as well as audio transcription. More info on these three projects below:
Makris D., Agres K., Herremans D.. 2021. Sen2Seq: A Conditional seq2seq Framework for Generating Lead Sheets with Sentiment. Proceedings of the International Joint Conference on Neural Networks (IJCNN). Download preprint.
