The startup develops AI-driven digital audio processing tools that utilize real-time neural signal processing to enhance audio quality for Gen Z musicians. By modeling complex non-linear processes, the technology enables music producers to create superior audio products efficiently.
Funding
$800K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional digital signal processing (DSP) methods struggle to meet the growing demand for high-quality, real-time audio processing due to their reliance on fixed algorithms and linear processing, which are inadequate for modeling complex interactions. Developing custom DSP code is time-consuming, costly, and lacks the adaptability to evolve and improve over time.
Solution
Waveshaper AI offers a suite of scalable, real-time audio processing solutions powered by a proprietary neural network backend. This platform enables users to model complex, time-varying, non-linear audio processes, optimizing performance and adaptability. Waveshaper AI's platform allows users to build, train, and deploy real-time models without the complexities of traditional machine learning pipelines, DSP tuning, or hardware constraints. The platform offers pre-built models and the ability to create custom models, providing solutions for a wide variety of applications.
Target Audience
The primary customers are audio equipment manufacturers, professional audio plugin developers, automotive companies, and any application requiring real-time audio enhancement.
Features
- Pre-built AI models for common audio tasks, including denoising, voice enhancement, echo cancellation, spectral recovery, de-clipping, analog modeling, and custom mastering
- Studio: Train custom models with user-provided audio samples, eliminating the need for a dedicated machine learning team
- Edge AI: Optimized for real-time audio processing on low-powered devices with minimal footprint
- Flexible Deployment: Models can be deployed on-device, via live API, as VST plugins, or within mobile and web applications
- Low-latency processing: Delivers on-device audio processing with latency under 10ms, eliminating cloud dependency and the need for GPUs or DSP scripting
- TypeScript SDK and React chatbox plugin for rapid, low-code integration