// AUDIO AI & MACHINE LEARNING
Audio AI & Machine Learning
Development
We build and deploy ML models into production audio systems. From sound event detection to generative audio, we handle the full pipeline: training, optimization, and C++ integration.
// WHAT WE BUILD
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.
// THE HARD PART
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.
See our embedded ML work in our Case Studies.
// FAQ
Common Questions
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