A Primer for Device Makers
AI Has Changed Audio, But Not Audio Hardware (Yet)
AI has already transformed the way we process sound. Features such as stem separation, automatic transcription, and voice cloning have all helped speed up workflows for many audio professionals. Until recently, however, AI-powered audio processing has primarily occurred in the cloud, often leaving embedded engineers on the sidelines for some of the most promising advances.
The good news is that with recent innovations in hardware, models, and AI deployments, this is starting to change.
HERE ARE TWO OF THE BIGGEST REASONS WHY:
AI Accelerators—Advances in dedicated AI hardware, including embedded GPUs like those in NVIDIA’s Jetson series, neural processing units (NPUs) such as Google’s Coral Edge TPU, and SoCs with built-in AI cores such as Qualcomm Hexagon series, make it far more efficient to run machine-learning models on resource-constrained edge devices.
Model Compression & Quantization—Techniques such as 8-bit quantization, pruning, and knowledge distillation reduce model complexity and computational demands, making neural networks efficient enough to run on resource-constrained edge devices.
AI accelerators bring efficient machine-learning models to the edge.
WHY THE EDGE IS THE FUTURE OF AUDIO AI
While many of the current solutions are very effective, cloud-based AI audio features also come with major limitations:
- Privacy: Data privacy and security can be particularly challenging when services are provided over the internet.
- Latency: Cloud-based solutions often come with increased latency, which can make certain use cases untenable.
- Cost: Frequent communication with cloud-based models can be prohibitively expensive and consume large amounts of bandwidth and energy.
The alternative, of course, is to embed the models directly into the hardware. This allows processes to happen much closer to real-time, helps protect customer data, and improves overall efficiency.
INDUSTRY EXAMPLE: EMBEDDED STEM SEPARATION
Stem separation is one of the most promising opportunities for edge AI audio. These models split a mixed audio track into its constituent parts, such as vocals, drums, bass, etc., entirely on-device, delivering near-real-time isolation. On modern edge platforms, this pipeline completes in tens of milliseconds, ensuring both low latency and data privacy.
KEY METRICS AND CONSIDERATIONS
End-to-end latency: Critical for live applications, with targets typically <10ms for instruments and <50ms for vocal processing to maintain performer comfort and audience experience.
Real-Time Factor (RTF): A crucial metric for non-live applications that measures processing speed relative to audio duration (e.g., RTF of 0.5 means 10 seconds of audio is processed in 5 seconds). Target RTF varies by application, but the aim is generally RTF < 1.0.
Memory footprint: Both static (model size) and dynamic (runtime) memory usage must fit within device constraints, typically 1-16MB for embedded targets like pedals and up to several hundred MB for higher-end hardware.
CPU/GPU/DSP utilization: Peak and sustained processing load affect battery life, thermal performance, and ability to run alongside other applications. The target is typically <70% of available resources to allow headroom.
Signal-to-Distortion Ratio (SDR): SDR is an objective quality metric for stem separation, measuring separation effectiveness in decibels. Higher values indicate better isolation of target instruments.
Subjective audio quality tests: MUSHRA, A/B, or other approaches.
TRADE-OFFS AND CHALLENGES
The primary challenges in these types of implementations include model optimization to improve efficiency without sacrificing separation quality, managing memory constraints on embedded devices, and achieving real-time performance with limited processing power. Many of the most powerful stem separation models were originally designed for high-powered cloud environments and must be quantized to 8-bit or 16-bit fixed-point representations, requiring careful calibration to maintain audio fidelity.
Another challenge is the diversity of target platforms, from DSP-equipped smart speakers to resource-constrained guitar pedals, each requiring specific optimizations and implementation strategies.
AI embedded directly into audio devices boosts critical performance factors.
USE CASES FOR EMBEDDED STEM SEPARATION
Embedding AI-powered stem separation directly into an audio device opens up a range of low-latency, edge features without any cloud dependency.
Promising implementations include: Accessibility—On-device AI stem separation could dramatically improve clarity for listeners with hearing challenges. A device’s microphone array could capture ambient sound, a lightweight stem separation module would isolate and amplify speech stems in near real-time, and the cleaned audio could feed directly to a user’s headphones or hearing aids. Because processing happens locally, the voice would go through with minimal latency and total privacy, helping people stay engaged in noisy environments like cafés, classrooms, or public transit.
Instrument Isolation for Practice—A smart speaker could extract and mute specific instruments (guitar, bass, drums) from a stereo mix, giving musicians a hands-free practice partner.
Dialog and Music Effects Control—A soundbar could split a film soundtrack into dialogue, effects, and background music channels, allowing cinephiles to boost speech clarity or fine-tune sound-effect levels independently.
LOOKING AHEAD
As AI models and edge hardware improve, audio workloads will increasingly run on embedded systems, bringing AI processes closer to real-time. We may soon see exciting features like real-time on-device translation, stem separation and editing during live musical performances, adaptive noise cancellation in industrial IoT, and audio-based safety alerts in autonomous vehicles.
RESOURCES
music.AI | music.ai
White Paper:
Edge AI for Audio: Trends, Use Cases, and Predictions,” Music.AI, May 2025, https://audioxpress.com/files/attachment/2782
https://www.popakademie.de/media/?file=978_241021-ki-whitepaper-en.pdf&download
https://www.mdpi.com/2072-666X/14/5/897
PUBLISHED IN CIRCUIT CELLAR MAGAZINE • SEPTEMBER 2025 #422 – Get a PDF of the issue
