Why We’re Taking a Human‑First Stand on AI‑Generated Music — Qobuz Community
Artificial intelligence is transforming the music industry at an unprecedented pace. Faced with the explosion of AI-generated content and the legitimate questions it raises, Qobuz has chosen transparency. This charter establishes our framework: how we use AI, where we draw our lines, and what commi
Malleable Music Ensembles
A framework using generalized algebraic theories and categorical lenses to enable interoperable, local-first networked music ensembles.
2002: Last.fm and Audioscrobbler Herald the Social Web
Following in Amazon's footsteps, two student projects independently use 'collaborative filtering' to bring recommendations and social networking to online music; soon they will join forces.
The Algorithmic Flattening of Sound: Computational Evidence and Justice Implications of AI Music Homogenization
This paper audits whether large-scale generative music systems exhibit measurable musical homogenization relative to human-produced music, and develops a justice-centered account of why this matters. We audit two commercially deployed systems (Suno and Lyria 3) across four genres (Afrobeats, K-pop, Dance Pop, and Heavy Metal). For each system and genre, we generate 100 tracks and compare them against human corpora of equal size, using 72 music information retrieval (MIR) features and multiple diagnostics of dispersion, redundancy, and separability. We define homogenization as reduced acoustic variation in standard computational audio features including rhythm and timing, timbre/spectral shape, and dynamics, both within genres and across genre boundaries. We also generate tracks using only a genre name as the prompt, with no additional instructions, to reveal each system's default musical tendencies. The results show two structurally distinct homogenizing tendencies. Lyria reduces within-genre acoustic diversity, while Suno collapses the acoustic distinctions between genres without compressing within-genre spread. Neither system follows user prompts faithfully, indicating that the observed patterns reflect learned priors rather than prompt constraints. The two systems do not converge on a common acoustic profile and are more acoustically distant from each other than two random human subsamples would typically be. Nevertheless, a standard classifier distinguishes AI from human tracks near-perfectly on MIR features alone. We argue that these patterns matter not as an aesthetic curiosity but as a justice-relevant condition, shaping which musical styles become legible, valued, and economically rewarded as generated outputs increasingly circulate at scale.
The Website Reshaping Live Music, One Set List at a Time
On Setlist.fm, users track what songs artists play at concerts (and more). The availability of so much data has changed the ways musicians and fans experience shows.
Two tower models for retrieval of recommendations
Fourth post in this series on personalized recommendations
“It’s unfamiliar, intimidating, and seemingly impenetrable for producers raised on DAWs like Ableton Live - but it can unlock a whole new world of creativity”: I tried a music tracker and it rewired my brain (in a good way)
Beloved by artists from Aphex Twin to Deadmau5, trackers swap the trusty piano roll for a text-based, vertically-scrolling interface that looks like something out of The Matrix. Let's find out how deep the tracker rabbit hole goes
HAIM: Human-AI Music Datasets for AI Music Production Tracking Benchmark
As generative platforms such as Suno and Udio reach human-grade audio quality, the scope of AI's utility has expanded across the entire music production workflow. Beyond simple track generation, these advancements have catalyzed the adoption of AI-driven methodologies in diverse forms. These include vocal synthesis, arrangement, and professional mastering. However, current detection research remains largely confined to a binary `AI-or-human' paradigm. It fails to reflect the realities of contemporary music production workflows. In real-world production, AI tools are increasingly used to refine or master human-produced tracks, and human engineers likewise post-process AI-generated material to ensure professional quality. Moreover, users often employ adversarial tactics to bypass AI detectors, such as applying human mastering to AI-generated tracks. This creates a grey area that a simple binary classification fails to capture. In this paper, we define and investigate ``AI Music Tracking'': the challenge of identifying specific AI integration across the multifaceted spectrum of music production. To this end, we introduce HAIM, a dataset with diverse labels for stages of music production. It is designed to isolate stages of AI intervention, including hybrid production and agent-level tracking. Our evaluation of state-of-the-art detectors reveals systemic flaws. By releasing HAIM, we propose a new benchmark that shifts the field beyond binary classification toward a granular, structured evaluation of AI music.
rocksky.app/rocksky
A decentralized music tracking and discovery platform built on AT Protocol 🎵
This is definitely my feeling working with them on recommendation algorithm.
Mark Riedl
Fascinating experiment: current AI systems lack creativity to reliably pursue research arxiv.org/abs/2607.27191 - poor judgment about the bar for publishable research - uncreative responses in research design - ineffective backtracking from dead ends - poor resource awareness - instruction drift
Recommender Systems
Letting Users Choose Recommender Algorithms: An Experimental Study
Agentic Taste Modeling | lab notes #8
A Bluesky feed for one · Adam Wiggins
How the Substack feed is learning to understand your reading journey
