# Audio AI & Machine Learning Development

> Atlantic Audio Development integrates machine learning into real-time audio systems. Neural audio processing, sound event detection, generative audio, and on-device ML pipelines built in C++ and Python — from training to production C++ integration.

- Page: https://atlanticaudio.dev/audio-ai-development
- Agency overview: https://atlanticaudio.dev/index.md
- Contact: info@atlanticaudio.dev

## Audio AI capabilities

- **Sound Event Detection** — real-time classification and detection of audio events using neural networks. From onset detection to complex scene analysis, optimized for low-latency environments.
- **Generative Audio Systems** — neural synthesis, sample generation, and hybrid DSP/ML architectures for creative tools. Models that produce coherent, musically useful output.
- **On-Device ML Deployment** — model quantization, pruning, and ONNX/C++ inference integration for embedded hardware and resource-constrained platforms. We've shipped real-time inference on devices with 500MB RAM.
- **Audio Analysis & Intelligence** — pitch detection, timbre analysis, tempo tracking, and content-aware processing. Smart features that make audio tools more intuitive and powerful.
- **C++ & JUCE Integration** — we bridge Python model development and C++ production deployment. ML features integrated directly into your JUCE plugin or standalone application without sacrificing real-time performance.
- **Dataset Curation & Training** — end-to-end model development from raw data to production-ready weights. Dataset cleaning, augmentation, architecture design, training, and evaluation on your specific audio domain.

## Why audio AI is different

**The real-time constraint:** audio runs in real-time at buffer sizes of 64 to 512 samples. A model that takes 50ms to infer is useless in a plugin. Getting ML to work within this constraint requires specialized architecture choices, quantization, and sometimes custom C++ inference code.

**Why we're equipped for it:** we combine deep signal processing knowledge with ML engineering. We know where to use a neural model and where a classical DSP approach is faster and more predictable. That judgment prevents expensive mistakes.

## FAQ

### Can ML models run in real-time inside an audio plugin?

Yes, with the right architecture. We use model quantization, pruning, and efficient C++ inference to deploy lightweight neural networks that meet real-time audio latency requirements. Our embedded neural network work has achieved real-time inference on devices with 500MB RAM.

### What frameworks do you use for audio machine learning?

We work with PyTorch and TensorFlow for model development and training, and deploy using ONNX Runtime or custom C++ inference for integration into JUCE-based plugins and standalone applications.

### Can you train a model on our proprietary audio dataset?

Yes. We handle dataset curation, preprocessing, training, evaluation, and deployment. We work under NDA and all data remains confidential.

### What kinds of audio AI projects have you shipped?

Our portfolio includes a Sound Event Detection (SED) system deployed on embedded hardware, achieving 95% F1 score at real-time on a resource-constrained device. We also work on generative audio systems and intelligent audio analysis tools.

### Do you work with generative audio models?

Yes. We build generative audio systems including sample generation, procedural audio engines, and hybrid DSP/ML approaches that combine traditional signal processing with neural models for creative tools.

## Related services

- Audio plugin development: https://atlanticaudio.dev/audio-plugin-development.md
- Custom audio software: https://atlanticaudio.dev/audio-software-development.md
- Hire JUCE developers: https://atlanticaudio.dev/hire-juce-developer.md