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.

"What should I work on next?", is the question we are trying to answer in our latest paper.

The arrival of LLMs and foundational models have significantly changed the field of Music Information Retrieval (ISMIR Conference).

Many of the researchers in the field have had to pivot or adapt to the changing environment and the powerful tools that we now have available. The question many of us are asking is: what topics remain unexplored and are in need of solving?

The conversation around music and artist rights has never been more critical. Clear guidelines are urgently needed to foster progress in academia and industry alike, free from the risks of lawsuits and unethical practices.

I am thrilled to share that MidiCaps - A Large-scale Dataset of Caption-annotated MIDI Files, has been accepted at ISMIR Conference. The MidiCaps dataset is a large-scale dataset of 168,385 midi music files with descriptive text captions, and a set of extracted musical features. The captions have been produced through a captioning pipeline incorporating MIR feature extraction and LLM Claude 3 to caption the data from extracted features with an in-context learning task. The framework used to extract the captions is available open source on github.