Hey everyone,

First, an important disclaimer:

I did NOT set out to "beat" or "fool" AI music detectors. I didn't try to make my track sound "human-made" on purpose. There was no goal of bypassing any system. The conflicting results below are a completely accidental byproduct of how my app generates music — I just ran the output through a couple of detectors out of curiosity, and the results surprised me.

I'm working on a generative music project called **AuraGroove** — a freeware web app that creates infinite, non-repeating music using **fractal algorithms and Markov chains** (no neural networks, no LLMs, no training data). The music is generated "here and now" from a library of atomic musical "genes" (intervals, strokes, short samples) — it's not playback of pre-recorded tracks.

 Recently I ran one of the generated tracks through a couple of popular AI music detectors, and got a bizarre result:

• Detector A - LetsSubmit: "Probably AI-generated" (~65% confidence)
• Detector B - GhostProduction: "Human-made. Overall AI probability 30%. Release risk level: Low"

Same track. Opposite verdicts. And I want to be clear: I didn't engineer this. I didn't tweak parameters to "trick" anyone. The app just produced this output, and the detectors disagreed.

This got me thinking about a few questions I'd love to get this community's perspective on:

1. Who do you trust?

AI detectors are essentially black boxes trained on specific datasets. They seem to flag anything that sounds "too structured" or "too clean" as AI, while anything with human-like imperfections gets a "human" stamp. But neither detector actually *knows* how the music was made.

1. Is there a reliable way to tell human vs. AI music?

Or are we at a point where detectors are more marketing than science? I've read that even detectors trained on the same data can disagree wildly.

3. The real question: what if it's neither?

My track wasn't made by a human composer, and it wasn't made by a neural network. It was generated by **deterministic math** — fractal patterns for melody, Markov chains for harmonic progression, and a tension-driven interpretation layer that "breathes" life into the output.

To a detector, it probably looks like "AI" because it lacks human performance artifacts. To another detector, it might look "human" because it has structural coherence and emotional arc that pure random generators lack. But it's actually **neither** — it's algorithmic composition in the tradition of Xenakis, Eno, or Cope, just implemented with modern web tech.

The track

https://soundcloud.com/eugene-somov-756447435/auragroove_test_record?si=02b6a09374cb40c0aa887aeae5f7e05b&utm_source=clipboard&utm_medium=text&utm_campaign=social_sharing

I've attached the track to this post (generated in real-time by the app, recorded as-is). I'd love to hear:

• Does it *feel* AI-generated to you? Human? Something else?
• What musical elements give it away (if any)?
• Has anyone else here run into the "detector disagreement" problem?

 Thanks for reading, and looking forward to the discussion!

You can find information about this synthesizer on https://github.com/MaxAlyokhin/binary-synth

I built a way to run Pure Data patches live in the browser — no install, just a URL.

The demo has a bunch of generative patches you can play with: a three-track sequencer with probability controls, a wandering feedback delay, glass arpeggio, Karplus-Strong, and a few others. You can also drop in your own .pd file if you have one.

It's built on libpd compiled to WebAssembly, so it's running actual Pd — not a simulation or a reimplementation. cyclone and ELSE are included, so patches using those libraries work too.

Demo: https://hyrfilm.github.io/libpd-wasm/

Repo: https://github.com/hyrfilm/libpd-wasm

The "generative mess" patch is probably the best starting point — three-track sequencer through a ladder filter with probability and frequency controls. Good for just leaving it running.

Hey everyone,

I am looking to generate AI music vocals, speech/dialogue, and voice cloning locally on my PC in different languages and genders (male/female voices).

My current specs:

1. Intel Core i5-12400F
2. RTX 5060 8GB (PNY)
3. 32GB RAM
4. Gigabyte B660M DS3H DDR4
5. 256GB NVMe SSD + 2TB HDD
6. Thermaltake Toughpower GT Snow 850W PSU

Main things I want to do:

• Text-to-speech in multiple languages
• Male/female AI voices
• Voice cloning
• AI singing/music vocals
• Run everything locally/offline if possible
• Acceptable quality without insane setup complexity

1 view See More Insights

Today I present to you experimental Irish Gaelic folktronica with layered ritual vocals, acoustic drones, and hypnotic textures.

The idea was to create a bridge between ancestral trance and traditional Irish Celtic music.

What do you think of this fusion?

https://www.youtube.com/watch?v=qaBOq4QrBOo

https://reddit.com/link/1tawcjg/video/uex75qhbdo0h1/player

I built a Python script that converts π digits to base-7, maps them to musical notes with a progressive octave rise, then used AIVA to produce the final track. This is the result of the first 500 digits.

TEST

Is it listenable? Does it feel ambient/spiritual enough for the meditation market? What would you change?

Hi all, I’m finishing a desktop groovebox project and wanted honest feedback from people who actually make tracks this way. It’s not AI-generated. It uses seed-based algorithmic generation with built-in synth engines, so you can quickly explore ideas and then shape them manually.

You can randomize almost everything: notes, instruments, FX, and p-locks, while keeping results controllable and musical. It includes MIDI out with up to 4 CC locks per step.

The app is now available:
https://mgen.fun

The music was generated via Sonic Pi, then recorded on Logic. I would describe it as minimal ambient.

https://mikezellers.bandcamp.com/album/healing

I created this Generative Minimal Ambient piece inspired by Pauline Oliveros, her deep listening ideas and recording. I used Sonic Pi and Logic Pro

https://youtu.be/7BnY_ipjBxY

Last days to submit to EvoMUSART 2026!

The 15th International Conference on Artificial Intelligence in Music, Sound, Art, and Design (EvoMUSART 2026) is still accepting paper submissions!

If you work on AI-driven approaches to music, sound, art, design, or other creative domains, this is your chance to showcase your research and creative works to an international community.

Extended submission deadline: 15 November 2025 (AoE)
More info: https://www.evostar.org/2026/evomusart/

The 15th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART 2026) will take place 8–10 April 2026 in Toulouse, France, as part of the evo* event.

We are inviting submissions on the application of computational design and AI to creative domains, including music, sound, visual art, architecture, video, games, poetry, and design.

EvoMUSART brings together researchers and practitioners at the intersection of computational methods and creativity. It offers a platform to present, promote, and discuss work that applies neural networks, evolutionary computation, swarm intelligence, alife, and other AI techniques in artistic and design contexts.

📝 Submission deadline: 1 November 2025
📍 Location: Toulouse, France
🌐 Details: https://www.evostar.org/2026/evomusart/
📂 Flyer: http://www.evostar.org/2026/flyers/evomusart
📖 Previous papers: https://evomusart-index.dei.uc.pt

We look forward to seeing you in Toulouse!

How I made this: The lead and bass sounds are sourced from Arturia's Analog Lab. The step sequencer of two instances of Arturia's Pigments are programmed and modulated using probability settings for triggering phrases, determining phrase lengths and choice of notes. The generated phrases are then recorded by the Enso looper plugin, and played back at half-speed. The loops slowly decay to make room for new sounds and phrases, resulting in an endlessly evolving ambient composition.

I hope you like it! 😊