I’m thrilled to introduce text2midi, an end-to-end trained AI model designed to bridge the gap between textual descriptions and MIDI file generation! Our paper has been accepted in the Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI), and will be presented in Philadelphia the coming month.

Exciting news from the AMAAI Lab at this year’s ISMIR conference in San Francisco! We were thrilled to showcase some of our research:

MidiCaps
Presented by Jan Melechovsky and Abinaba Roy, MidiCaps is the first large-scale open midi dataset with text captions. This resource will enable us to develop the very first text-to-midi models (stay tuned -- our lab's model is coming soon!).

The AMAAI Lab was honoured to receive Prof. Kejun Zhang and Jiaxing Yu from Zhejiang University last week.

It was fascinating to hear about their latest research in multimodal AI, affective computing for music, latest large datasets, and cool image-based music generation interfaces. I am looking forward to future collaboration!

AMAAI: Abhinaba Roy, Ph.D., Renhang Liu, 路通宇, Geeta Puri, Sithumi Kavindya, Jan Melechovsky, Charlotta-Marlena Geist

How come music and emotion are so intrinsically connected, yet, any music emotion prediction model has sub-par performance? We talk about the current challenges in the field and provide a comprehensive list of music-emotion datasets as well as recent models.

We also kept these lists in a community GitHub so they can be kept up to date: https://github.com/AMAAI-Lab/awesome-MER. If we forgot any model or dataset: just do a pull request to add yours!

I am excited to announce that Dr. Joel Ong has successfully defended his thesis on Modern Portfolio Construction with Advanced Deep Learning Models as part of the AIFi Lab at SUTD. It has been a pleasure to supervise Joel and guide him through his exploration of various multitask architectures and mixture of expert models, all aimed at leveraging deep learning for effective portfolio construction.

You can read the full thesis here or below the abstract.

The Music, Audio, and AI Lab (AMAAI) at SUTD invites applications for a PhD position in the exciting and rapidly evolving field of music and audio artificial intelligence.

The AMAAI lab, led by Prof. Dorien Herremans, is engaged in cutting-edge research at the intersection of music, audio, and artificial intelligence. We are based in tropical Singapore, a hub for AI research and startups, but also a great base to explore South East Asia, where English is the first language. Our PhD students and PostDoc researchers work on projects that explore areas such as:

I am excited to announce the latest paper by Viet-Toan Le, who was a visiting student at the AMAAI Lab, on 'Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey'. Viet-Toan did an amazing job at collating and nicely presenting over 225 papers in Music Information Retrieval that are inspired by NLP, as well as presenting the latest challenges and steps forward for the field!

Excited to announce Mustango, a powerful multimodal Model for generating music from textual prompts. Mustango leverages a Latent Diffusion Model conditioned on textual prompts (encoded using Flan-T5) and various musical features. Try the demo! What makes it different from the rest?
-- greater controllability in the music generation.
-- trained on a large dataset generated using ChatGPT and musical manipulations.
-- superior performance over its predecessors as per the experts.
-- open source!

We are happy to announce two talks on Tuesday 17 October at 2pm at SUTD I3 Lab 1.605

Add to your calendar.

Title : Exploring NLP Methods in Symbolic MIR: Representations and Models