You can make a song today with a laptop, an instrument and a microphone, and some people make them on just a phone. Finishing that song so it holds up next to everything else on a playlist is much harder. That stretch, from rough recording to release, is the last mile of music production. We spun off Sequential AI, to help you master a song with easy text controls. Are you a musician? Try our open beta now.

Read how it works.

When a rock band's intensity peaks, the camera cuts to close-ups. When the scene fades to slow motion, the music softens. We all sense this relationship, but can AI models actually reason about it causally?

We introduce KARMA-MV, a benchmark for causal question answering on music videos.

The Audio, Music, and AI Lab (AMAAI) at Singapore University of Technology and Design (SUTD) is seeking a talented Research Assistant (or potentially a Postdoc) — essentially a coding wizard — to join us in pushing forward large-scale generative multimodal AI models, with a strong emphasis on music and audio.

Excited to share that my research impact placed me in the Top 2% of Scientists Worldwide in the 2024 Stanford/Elsevier ranking.

Grateful for the amazing collaborators and support that made this possible!

Ever struggled with cleaning up home-recorded music? Issues like weird echoes, distortion, uneven sound, or mastering to bring your music up to production-level quality can be a huge pain to fix — usually needing several different tools and lots of tweaking.

We just released SonicMaster, a model that aims to simplify this process by handling all those common problems in one place. The coolest part? You can control it with simple text instructions ('Make the audio smoother and less distorted.'') or let it automatically restore your audio.

Together with the AIM at QMUL, the AMAAI Lab is organizing the First Workshop on Emerging AI Technologies for Music (EAIM 2026) as part of the AAAI conference 2026 in Singapore.

The workshop will bring together researchers, industry leaders, and practitioners working at the intersection of AI and music. We’ll explore how advances in generative models, multimodal learning, personalization, explainability, and human–AI collaboration are shaping the future of music creation, analysis, and interaction.

I am thrilled to announce two PhD fellowship opportunities at the Singapore University of Technology and Design (SUTD) for talented Thai students, Singaporean students, and Singapore Permanent Residents (PR). These fully-funded positions are available in the fields of AI for Finance and AI for Music, hosted by the AIFi Lab (AI for Finance) and AMAAI Lab (Audio, Music, and AI Lab), respectively. We are seeking exceptional candidates with a passion for AI and strong academic backgrounds to join our vibrant research community.

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!).