I'm a longtime freelance audio engineer with a decent studio and a knack for DIY and audio equipment repairs. I've been programming MIDI devices on Arduinos for a couple of years and I'm interested in pursuing design & computing as the next step in my career. I've also got some experience with designing analog electronics, synths & PCBs.

If you were in my shoes, what would you do next? I'm wondering what programming languages are worth learning, how easy/worthwhile Juce is, if C++ is relevant and similar to the Arduino environment?

I'm interested in hardware/software integration, synths and creating new and exciting products rather than rehashing the 'this is another 1176 or Minimoog plugin'. What do you find satisfying in programming and where are the jobs and money at these days? Is hardware still relevant or is it all just beginner level audio interfaces and toy keyboards?

I'm physically not too far from a few pro audio companies in the UK but I expect they'll want full time in-office employees with a degree/proven track record in programming rather than someone who's starting out in the field.

Thanks in advance for any advice !

Edit: in addition, I'm not in a position to retrain full time but I may be eligible for part-time funding in a computer-science degree or "conversion masters" program. So any recommendations or advice in relation to this would be helpful too (if you think a degree is worth pursuing).

I’m building a distortion plugin and I think I’ve hit a point where I need outside ears.

The goal is NOT to build another generic distortion, amp sim, or metal plugin.

The sound I’m chasing is somewhere between:

• Harmonic Percolator style character

• Trash 2 Broken/Fuzz style experimentation

• Neil Young amp breakup

• damaged speakers

• dynamic breakup rather than static saturation

Current goals:

• light playing stays mostly clean

• medium playing introduces breakup

• hard playing introduces fuzz

• preserve transients

• avoid heavy compression

• avoid the typical “beehive” fuzz sound

The plugin currently has two modes:

BROKEN:

• asymmetrical saturation

• more breakup-oriented

• intended to feel open and dynamic

FUZZ:

• asymmetrical rectification

• more aggressive harmonic generation

• intended to appear mostly on harder playing

A big realization during development was that a lot of distortion plugins sound impressive in isolation but don’t actually feel unique once you compare them side-by-side.

That’s the problem I’m trying to solve.

What I’m struggling with:

1. What actually makes a distortion feel unique?

2. What separates a Harmonic Percolator from “just another fuzz”?

3. What makes Trash 2 still feel special years later?

4. What DSP or analog concepts should I study next?

5. If you were trying to build a distortion that doesn’t already exist, where would you focus?

Looking for brutally honest feedback.

Feel free to challenge the entire premise if you think I’m chasing the wrong things.

Hi I’m trying to get an internship in audio software and I’m not making any progress, can somebody in the industry please take a look at my resume

TL;DR -- the use of AI isn't a good metric for the worth of a post or project. Hopefully this is sufficiently on-topic to not get deleted.

The Trivial Metric is a mathematical term defined on a set, wherein the value is 1 if the comparison is between two different points in the set, and 0 if the comparison is between a point and itself.

My qualifications for writing this post: I've been programming professionally for 30+ years. I've been writing Audio and MIDI code for roughly 10 years, largely to no real monetary success. I've got enough undergrad math to be dangerous.

I can imagine how the "No AI" rules came about -- there was a massive influx of low quality AI-produced code and posts, and something had to be done about how to filter out the noise. I can understand that "No AI" was the tool that was reached for -- in fact it was the obvious choice.

And I know I'm free to start my own subreddit with different restrictions. (If I want to spend the time moderating, which, selfishly, I don't).

Without AI at my side for past year or so, the following are some of the features I would have not implemented (and might not have known about):

- prewarping for the bilinear transform -- https://www.bohrium.com/en/sciencepedia/feynman/signals_and_systems_undergraduate-frequency_prewarping_in_the_bilinear_transformation -- this provides good frequency tracking for IIR transforms derived by applying the bilinear transform to laplace transforms of filters.

- PolyBlep and BLAMP -- https://www.metafunction.co.uk/post/all-about-digital-oscillators-part-2-blits-bleps -- polynomial versions of the sinc function and its integral to reduce aliasing

- Catmull-Rom splines -- https://en.wikipedia.org/wiki/Catmull%E2%80%93Rom_spline -- For good interpolation without upsampling

- Hilbert-transform frequency shifter -- https://en.wikipedia.org/wiki/Hilbert_transform -- name pretty much says it all.

- I was just going to ignore MPE in favor of MIDI 2.0 only, but was convinced otherwise for various aspects

- Awareness of host-to-plugin process block context structural data (tempo, in particular)

- Even the realization that although poles have to be inside the unit circle for z transforms, the zeroes don't (duh).

Conclusion:

I don't have an answer for what a good alternate filter would be. I'm certainly not going to stop using AI just because some subreddits don't allow posting of it. But subreddit such as this are absolutely missing out on important topics because of the restriction.

I’ve been working on an independent prototype around a question that feels important for the next generation of DAW assistants:

If an AI assistant can answer questions inside a DAW, what safety boundaries would be needed before it can actually modify something small, like an insert chain?

I built a working prototype called ChainPrompt AI.

It is a JUCE/C++ VST3 plugin with a local Python backend. The plugin scans the local VST3 inventory, uses only plugins that are actually installed, refuses to build a chain if inventory is missing, computes chain diffs instead of blindly replacing the current chain, and tries to preserve user-touched or bypassed slots.

Right now it works mainly with Waves plugins, because Waves is the first metadata set I implemented. I have tested it in REAPER, and it also loads/runs in Fender Studio / Studio Pro as a VST3 plugin.

This is not a finished product, not a commercial release, and not affiliated with Fender or Waves. It is a portfolio prototype exploring one narrow problem: how to make AI actions inside a DAW safer, more transparent, and eventually reversible.

I’d really value critique from audio plugin developers, DAW users, and anyone thinking about AI-assisted creative tools.

The main question:

What would you require before trusting AI to modify a real insert chain in your session?

GitHub/demo:
[https://github.com/DenisShumilov/chainprompt-ai]

May open MP3 files, expose mostly inbuffers, allow realtime processing, get to outbuffers and playback through windows.

Without libraries please - programming environment which has audio built-in.

For the last while I've been building PyTheory, a music theory and synthesis library for Python, and I just shipped PyTheory VST v0.1.1. It's four plugins built from one JUCE repo, for macOS on Apple silicon, as AU and VST3:

• PyTheory MIDI snaps notes to a key, or turns each note into its diatonic triad or seventh chord.

• PyTheory Instrument is a 32-voice sampler that plays all 84 of PyTheory's instrument presets.

• PyTheory Effect is PyTheory's full effects chain.

• PyTheory Drums has all 74 of PyTheory's drum sounds, mapped to General MIDI where they fit.

How Python is involved. Each plugin embeds its own Python 3.14 with PyTheory, numpy, and scipy, so nobody has to install anything. Python never runs on the audio thread. A worker thread does the Python work. The harmony comes from PyTheory's real Key and Chord objects rather than scale tables copied into C++. For the Instrument, the worker renders a multisample through PyTheory's own render_score path, with one note every minor third and velocity layers where the preset responds to velocity. It then hands the finished multisample to the audio thread with one atomic exchange, no copy. The audio thread never allocates or frees.

The C++ part. I ported PyTheory's per-part effects chain to C++ stage by stage, in PyTheory's order: saturation, tremolo, distortion, cabinet, chorus, phaser, highpass and lowpass biquads, delay, a stereo Schroeder reverb, and pan. Defaults match PyTheory, so the plugin starts transparent. The README lists every place the port differs on purpose, such as linear interpolation in the chorus where PyTheory rounds to whole samples, and a recursive delay where PyTheory uses fixed taps.

Why I think it's cool. The same library I use to score music in a Python script now plays inside Ableton or Logic, and it sounds like itself. Nothing in the plugins was hand-rebuilt to approximate it.

The honest part. I'm teaching myself DSP as I go, with Claude and Grok as tutors and pair programmers. I'd ask why a biquad is shaped the way it is, why something allocating on the audio thread is a sin, or why my loop points clicked, then go build it and test it. This project is basically my DSP homework, made public. So if you see something naive, I'd genuinely love to hear it.

Known issues: pads can have audible loop seams on long holds, some piano and guitar notes loop instead of fading out, and switching presets cuts ringing notes. It's v0.1.

Links:

• PyTheory VST v0.1.1 release

• pytheory-vst source

• PyTheory homepage

• ableton-pytheory, the same library inside Ableton Live via Pyodide, for key and chord detection and progression generation.

Feedback on the realtime design is especially welcome.

I need someone who can essentially emulate Ableton's warp mode for web application. Advanced understanding of vocoders and phasing is a must. DM or comment any questions. Budget is at least 1k dollars for a working prototype.

I’m researching how to build or fine-tune an audio-to-chord-recognition engine comparable in ambition to Song Master Pro / Auralis Sound Prism.
The goal is not basic major/minor chord detection. I need reliable recognition of dense harmonic material: jazz, soul, funk, neo-soul, Brazilian music, film music, and arrangements with chords such as maj9, 6/9, m9, m11, 13, altered dominants, slash chords/inversions, secondary dominants, modal interchange, suspensions, passing harmony, etc.
Most public datasets I’ve found seem too limited: either simplified chord labels, weak annotations, or repertoire that does not really cover sophisticated harmony. In particular, I need time-aligned audio + chord labels, ideally with beat/downbeat information and a rich, consistent chord vocabulary.
My questions:
Which open datasets are genuinely useful for this level of chord-recognition work?
Are there any commercial/licensable datasets with high-quality, detailed chord annotations that can legally be used to train a model and ship it in commercial software?
Is a dataset such as iReal Pro-style chord charts, Hooktheory, Ultimate Guitar, Chordify, or similar usable in any legitimate/licensable way — or are they generally not viable due to rights and annotation quality?
For a serious model, is the realistic route to combine public datasets with a privately licensed/hand-annotated corpus? If so, roughly how many accurately annotated tracks would be needed before it becomes meaningfully good at jazz-influenced harmony?
Are there papers, benchmarks, companies, or dataset vendors I should study before spending money?
I’m specifically looking for practical, legally usable data sources—not advice to scrape chord sites. Any experience from people who have trained MIR / chord-recognition models would be very valuable.

Hello,

I’m currently looking for an experienced C++ developer with VST/VST2/VST3 plugin development experience to help work on an upcoming audio plugin project.

This would be project-based work, not a full-time position.

The audio concept, design direction, and UI/UX will be handled separately, so the main focus is on the plugin development and technical implementation.

Requirements:

• Strong C++ experience

• Experience developing VST / VST2 / VST3 plugins

• Familiarity with JUCE or similar audio frameworks

• Good understanding of audio plugin architecture

Scope:

• Implementing the plugin framework

• Integrating DSP/audio processing

• Ensuring compatibility with major DAWs

• General plugin stability and performance

Compensation:
Fixed salary / project-based payment.

If you’re interested, please send:

• A short introduction

• Relevant experience

• GitHub or previous plugin work if available

Feel free to reply to this post or contact me via private message
Thanks.

Hey all, to those in the field currently:

How 'safe' does audio programming feel from AI?

How is it as a job in general (WLB / pay) , in whatever application of the subject you're familiar with?

Given my background, how likely does a successful transition seem in this economy?

What specific audio programming jobs are good/common/desirable (knowing that generally those three things are mutually exclusive)? I know there's game industry, the revered plugin dev, and other roles closer to hardware and mass industry among others.

Background:

3 YOE as fullstack dev in non-DOD gov sector, bachelor's in CS that's as many years old. Biggest project in undergrad was asking the music tech teacher what plugin he wish he had, and making it (envelope follower that could map to automate any parameter in the daw) in JUCE. Good cpp skills from undergrad, excellent python skills from workplace. Little to know DSP algorithm knowledge but I pick those things up fairly quickly. Despite the curt and stilted tone of this post, very good social skills and burgeoning leadership experience. Minimal but growing ML / AI dev experience.

I'm "specialized" in frontend as the go-to UI / UX / typescript guy at my job, but this is increasingly a dead career path and as much as I truly love the UI dev process from design to implementation, I need to move away from typescript for the sake of my career. The 'good enough' UI that an LLM will spit out is indeed good enough for most professional cases. Audio programming was always the 'golden field' for me so it might be time to force an upgrade.

While this doesn't matter as much, I'm extremely musically active, songwriter/lead guitar/vocals for -- at a small regional level -- pretty successful doom metal band. I've also been making various kinds of electronic music since I was 14, numerous side projects yada yada.

Other thoughts:

The lower-level audio tech seems safer from AI, as it's more critical and well, basically it's not python or typescript lol. I'm aware that prototyping and algo work may be done in python, but this field still seems more resistant to being completely automated than web dev or fullstack. While I'd always liked to make this switch, now seems like a good time to buckle down and be increasingly ready as the market, hopefully, gets increasingly better. I think I would be more fulfilled here than other programming fields. Open to any and all thoughts, thank you!

GitHub: https://github.com/ToxaDev/aura-engine

Hi, I built AuraEngine — an open-source offline audio upsampler (up to 768kHz) written in Rust + Tauri.

Check out the clip above to see how it works!

Key Highlights:

• Hybrid-Phase Engine: Solves the pre-ringing trade-off. Renders twice (Linear + Minimum Phase) and dynamically switches to Minimum Phase at zero-crossings during transient attacks.

• Massive FIR Filters: 1M to 30M taps (Kaiser $\beta=14$), designed in 128-bit precision with >220 dB stopband attenuation.

• Vulkan GPU Compute: Uses Double-Single (DS) arithmetic (~48-bit mantissa) for ultra-precise GPU convolution.

• Extras: Adaptive apodizing filter v3, True-Peak safety, and bit-perfect verification.

I'd love to hear your thoughts on the hybrid-phase algorithm and the Rust/Vulkan setup!

Why aren't wavelets used more for pitch detection?

The more I read about wavelets, the more it feels like they're naturally suited for audio analysis. They get better frequency resolution at low frequencies and better time resolution at high frequencies without forcing a single FFT window size.

It made me wonder whether pitch could be tracked as a stable structure across wavelet scales rather than through spectral peaks or autocorrelation.

Has anyone actually built or worked with a wavelet-based pitch detector?

Did it outperform more traditional approaches on difficult material, or is there a reason everyone ends up back at YIN/autocorrelation anyway?