Pedro Sarmento

Vibe coding is cool but have you tried vibe patching? Pure Vibes = Pure Data + MCP. Describe your sound, watch the patch appear 🌊 🔊👇

music - AI - ethics 🎸

SCIENCE PAPER DROPPED big ups @zacknovack 🥳 & the @harmonai_org team This paper explores an inexpensive method to add custom control (e.g. pitch) to a pretrained audio diffusion model, without retraining the model arxiv.org/abs/2603.04366

Some really great insights here about the differences between masked and uniform-state discrete diffusion. Both continuous diffusion and uniform-state discrete diffusion for modelling categorical data seem to be making a bit of a comeback recently. Entropy is all you need🙃

@CasebeerJonah @__gzhu__ @zhepeiw03 @NicholasJBryan Really interesting work! Will there be a link where we can listen to some results or check the code?

GenAE: An audio autoencoder engineered for generative modeling. To appear at ICASSP 2026. w/ @__gzhu__ @zhepeiw03 @NicholasJBryan arXiv: arxiv.org/abs/2602.15749 Video: youtu.be/gDIIuLb0cf0

@Ashvala @nonety_pe @tldraw wow this is super cool!

Coming soon to @nonety_pe: draw to engrave the music. Built on top of everyone' s favorite drawing tool, @tldraw and a simple 300k parameter CNN.

Excited to announce our ICASSP 2026 paper "Stemphonic: All-at-once Flexible Multi-stem Music Generation" ! w/ @__gzhu__, @j_p_caceres, @huangcza, and @NicholasJBryan 🔊Demo stemphonic-demo.vercel.app 📰Paper arxiv.org/abs/2602.09891 More details in🧵

The ACE-Step 1.5 paper can be confusing. In this post I share are its main ideas: artintech.substack.com/p/ace-step-15-… 1. DIFFUSION MODEL: supports multiple tasks. 2. LANGUAGE MODEL: reprompting & semantic tokens generation. 3. DATA PREPARATION: 27M songs. 4. OPEN WEIGHTS: supports LoRAs.

Yes! This ATTM Grand Challenge brings the fair-play & affordability we've been longing for in TTM research: from-scratch training on fixed academic data, prioritizing novel algorithmic or system design. We provide a MeanAudio baseline to get started easily. Join us! 🚀 #ICME2026

New paradigm from Kaiming He's team: Drifting Models! With this approach, you can generate a perfect image in a single step. The team trains a "drifting field" that smoothly moves samples toward equilibrium with the real data distribution. The result? A one-step generator that sets a new SOTA on ImageNet 256x256, beating complex multi-step models.

Today we’re releasing the International AI Safety Report 2026: the most comprehensive evidence-based assessment of AI capabilities, emerging risks, and safety measures to date. 🧵 (1/17)

🔊 New Paper Alert — Training-Free Inference-Time Timbre Transfer Check out our latest #ICASSP paper on timbre transfer in music audio! 🎶 ➡️ Diffusion Timbre Transfer Via Mutual Information Guided Inpainting; Ching Ho Lee, Javier Nistal, Stefan Lattner, Marco Pasini & George Fazekas 📜 Paper: arxiv.org/pdf/2601.01294 🥁 Audio Demos: anon-audio-demo-25.github.io/audio_demo/ In this work, we rethink timbre transfer as an inference-time editing problem — and show that you don’t need to retrain or fine-tune heavy models to change the instrumental color of a piece while preserving its musical structure. 🎯 What’s New? Instead of training separate models or adding control modules for each instrument: ✅ We start from a pre-trained latent diffusion model and steer it on the fly using two simple controls: • Mutual-Information guided noise injection: add noise only in latent channels most informative of timbre. • Early-step clamping: “lock in” melody and rhythm by restoring structure-dominant channels during denoising. This lightweight, training-free procedure lets you control timbre without sacrificing the original melody, harmony or rhythm — and works with text or audio conditioning (e.g., CLAP). ✨ Why It Matters 🎵 Practical music production tools for re-orchestration and sound design 🛠️ Efficient editing with no added model training 🔍 A framework that could extend beyond timbre to other label-driven audio edits 📌 Compatible with strong diffusion backbones and generative audio models @SonyCSLMusic @SonyCSLParis

Very cool to see this large dataset. Walking is good! Bummer about swimming though! My preferred exercise. I figure variability might be greater with swimming! @OncoAlert

Carlos Hernandez-Olivan, Hendrik Vincent Koops, Hao Hao Tan, Elio Quinton, "Single-step Controllable Music Bandwidth Extension With Flow Matching," arxiv.org/abs/2601.14356

if you're into great music in general, and prog metal in particular, check the new single by titan @jackjamesloth 🤘 servalprog.bandcamp.com/album/gone

@ArxivSound are we running out of cool acronyms? 🙃 arxiv.org/abs/2506.17815

Xinhao Mei, Gael Le Lan, Haohe Liu, Zhaoheng Ni, Varun Nagaraja, Yang Liu, Yangyang Shi, Vikas Chandra, "SLAP: Scalable Language-Audio Pretraining with Variable-Duration Audio and Multi-Objective Training," arxiv.org/abs/2601.12594

Coditany of Timeness --- In 2017 we made the 1st fully neural synthesized album. Ever. We put it on Bandcamp. 1M+ people listened. 100+ articles were written about it. It was research. It was art. It was anti-human black metal. Today Bandcamp BANS ai 🚫 reddit.com/r/BandCamp/com…

Simon Rouard, Manu Orsini, Axel Roebel, Neil Zeghidour, Alexandre D\'efossez, "Continuous Audio Language Models," arxiv.org/abs/2509.06926

AI music doesn’t have to be ‘slop’. It can be Interactive! Over the break, I wrote a post defining Interactive AI Music, a term that brings together my favorite artistic projects in AI and music. artintech.substack.com/p/interactive-…

TIL torvalds is into audio effect programming github.com/torvalds/Audio…