Getting Started Making Music | Notion

The Millions of Songs Mashed Into AI-Generated Music

Explore the astonishing amount of music available to AI developers.

Sine, Saw, Square, Triangle, Pulse: Basic Waveforms in Synthesis and Their Properties

Learn the properties of 5 basic waveforms in sound synthesis to use their full potential in your synthesizer performance or design.

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

Music has Meaning

The social platform for discovering music, collecting tracks, and supporting artists directly. Only verified artists post.

[Interview] Snow J of Cheerful Music dives into the cross cultural intersection of AI and music – EARMILK

In conversation with Earmilk, Snow. J explores how AI has restructured the foundation of music culture, striking a balance between AI and human-made music as well as Cheerful Music's distinct approach to engaging AI technology in music making and marketing.

Malleable Music Ensembles

A framework using generalized algebraic theories and categorical lenses to enable interoperable, local-first networked music ensembles.

AI music tools challenge Bay Area musicians

AI music tools challenge Bay Area musicians: As affordable AI platforms spread, artists debate creativity, copyright and survival.

newmusicusa.org — Semble

Semble page for newmusicusa.org

Treble and Bass Clef Notes: The Essential Guide to Sheet Music -

"Dive deep into treble & bass clef notes! Unlock the mysteries of musical notation & elevate your music reading skills."

Art of Noise

A multi-sensory exploration of how design has changed the way we’ve experienced music over the past 100 years.

Speaking of Music

“Can anyone explain in mere prose the wonder of one note following or coinciding with another so that we feel that it is exactly how those notes had to be? Of course not.”

From Prompting to Describing: A Cross-Cultural Study of Language for AI-Generated Music

Recent text-to-music (TTM) systems such as Suno [22], Udio [23], and Google’s MusicFX [7] allow users to generate music from a natural language prompt, lowering the barrier to music creation for users without specialized musical knowledge [27]. Understanding how users write these prompts is therefore a fundamental question for music information retrieval and human-AI interaction research. Yet, prompting a generative system is not the same cognitive or linguistic act as describing music one has heard [12]. When a user writes a prompt, they encode anticipatory intent to steer the system toward a desired or imagined output, whereas when a listener describes a piece of music, their language is grounded in a perceptual experience. We argue that these two acts produce systematically different language—a distinction that, despite being intuitive, has not been quantitatively examined. This gap has practical consequences: recent TTM models are trained predominantly on metadata-centric corpora (genre labels, BPM, descriptive tags) [6] rather than on the kind of language users naturally produce when listening, leaving the prompt-description gap unexamined. Moreover, this gap limits our ability to evaluate and improve human–AI interaction in music generation, as current systems are optimized for prompt input but commonly assessed through human perception.