- Twitter-based Bitcoin extreme movement predictions with PreBit
- Time-series momentum portfolios with deep multi-task learning
- DiffRoll - Music Transcription with Diffusion
- New paper on the emoMV datasets published in Information Fusion
- Keynote at AIMC
- New paper in Sensors on Single Image Video Prediction with Auto-Regressive GANs
- Bitcoin extreme price prediction with finBERT & Twitter
- Job opening: game developer (unity)
- Our cough models featured in NRF magazine
- Seminar on music and AI at KTH
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.
Recent PhD graduate Dr. Abigail Lee-Leon, Prof Chau Yuen, and myself just published a paper on 'Underwater Acoustic Communication Receiver Using Deep Belief Network' in IEEE Transactions on Communications. Preprint link. Underwater communications is a challenging field due to the many interferences in the channel (e.g. Doppler effect, boats, fish, etc.). This paper uses a novel deep learning approach to model the receiver.
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.
Congrats Kin Wai (Raven), on this interesting paper on leveraging spectrogram reconstruction for music transcription, which was accepted at the International Conference on Pattern Recognition (ICPR2020). Read the preprint here.
Cheuk K.W., Luo Y.J., Benetos E., Herremans D.. 2021. The Effect of Spectrogram Reconstructions on Automatic Music Transcription: An Alternative Approach to Improve Transcription Accuracy. Proceedings of the International Conference on Pattern Recognition (ICPR2020).
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!
Our team at Singapore University of Technology and Design (SUTD) is looking for an RA for 6 months to help develop nnAudio. You will be joining our team in music/audio/vision AI supervised by Prof. Dorien Herremans. More information on the music/audio team at dorienherremans.com/team. You will be working on the PyTorch audio processing tool nnAudio developed by Cheuk Kin Wai at our lab.
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
Congrats to Rui Guo, who was an intern at AMAAI Lab, SUTD, published a paper on 'A variational autoencoder for music generation controlled by tonal tension', which will be presented next week at 'The 2020 Joint Conference on AI Music Creativity'.
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.
Your goal is to develop novel approaches to neural reasoning starting from textually defined tasks in 50% of his/her time and to work on multimodal sarcasm detection in the other 50% time. You will be working with Prof. Alex Binder, Prof. Soujanya Poria, and Prof. Dorien Herremans.
The first task on neural reasoning will include work packages such as:
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
Thao Phuong, PhD student supervised by Prof. Gemma Roig and myself just passed her preliminary exam! Thao's work is on predicting valence and arousal from both video as well as audio. Her multimodal models have been published (and some more under review). You can read about them here.
Abigail Leon has successfully defended her PhD today with main supervisor Prof. Yuen Chau and myself as co-supervisor. Abigail has successfully explored how deep learning techniques can be used to denoise en demodulate complex underwater acoustic communication signals, and has performed some sea-trials to gather data for this. Since the PhD is under an NDA with Thales, we cannot post it, however, check out some of Abigail's papers here (more to come after the review process ends).
AI.SG is offering scholarships to top students who are interested in AI research. My lab at Singapore University of Technology and Design has positions for those interested in research on AI for audio/music/finance and NLP/emotion. More information about the lab and our projects: dorienherremans.com
Since this is an elite scholarship you can only apply if you have:
- high GPA
- Master's degree, or existing core ranked publications
Looking for a tool to extract spectrograms on the fly, integrated as a layer in PyTorch? Look no further than nnAudio, a toolbox developed by PhD student Raven (Cheuk Kin Wai): https://github.com/KinWaiCheuk/nnAudio
nnAudio is available in pip (pip install nnaudio), full documentation available on the github page. Also check out our dedicated paper: