aubio, a collection of algorithms and tools to extract musical meaning from audio signals, such as tempo, pitch, and onset
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.
Audacity Manual
■ How to use Audacity ■ Tutorials for Audacity ■ Searching the Manual
Welcome to AcoustID! | AcoustID
AcoustID is a project providing complete audio identification service, based entirely on open source software.
Free Online Music Maker & DAW – Make Music in Your Browser | Audiotool
Audiotool is a 100% free, browser-based DAW with no track or feature caps. Build modular signal chains, run VST3 plugins, and collaborate in real time — no install.
autolrc
finally, automatic lyric syncing that works. song + lyrics in, syllable-timed lrc out.
The Millions of Songs Mashed Into AI-Generated Music
Explore the astonishing amount of music available to AI developers.
SPEC.md at main · center.for.cybernetic.beauty/atmosynth-patch-spec
Specifications on how to store web audio graphs for music production in ATproto lexicons
Our New SAM Audio Model Transforms Audio Editing
We're introducing SAM Audio, a state-of-the-art AI model that enables you to segment sound.
Music Encoding Initiative
The Music Encoding Initiative (MEI) is a community-driven, open-source effort to define a system for encoding musical documents in a machine-readable structure. MEI brings together specialists from various music research communities, including technologists, librarians, historians, and theorists in a common effort to define best practices for representing a broad range of musical documents and structures. The results of these discussions are formalized in the MEI schema, a core set of rules for recording physical and intellectual characteristics of music notation documents expressed as an eXtensible Markup Language (XML) schema. It is complemented by the MEI Guidelines, which provide detailed explanations of the components of the MEI model and best practices suggestions. Read more …
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.
🎵 ATmusic
ATProto projects related to music. #ATmosphereConf
Tapes
FreeMix: Music. Liberated. For the open web.
onrepeat.fm
Musicat
Tempo Music
Malachite