Attention Labs

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Attention Labs is an Audio & Music tool that provides on-device selective auditory attention for voice AI. It routes only addressed speech before ASR runs, operating without a GPU and under 150ms latency.

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At a glance

Pricing

Custom · Likely Not Free

Free tier

API

Yes

Skill level

Technical

About

What is attention labs?

Attention Labs offers the Selective Attention System (SAS), an on-device pre-ASR selective auditory attention SDK designed for voice AI. SAS classifies incoming audio as silent, talking to a human, or talking to the device, routing only device-addressed speech to the ASR pipeline. This technology solves the 'cocktail party problem' for voice AI by suppressing bystander speech and reducing false triggers. It operates on ARM Cortex-A class CPUs without requiring a GPU, boasts under 150ms decision latency, and has a runtime footprint under 20MB. SAS supports Python, Java, and C++ APIs, and can be deployed on Linux, Android, Windows, macOS, and Raspberry Pi, making it ideal for constrained edge hardware like robotics and smart home devices.

Best used for

Ideal for developers and engineers building voice AI on embedded hardware who need to accurately route device-directed speech, reduce ASR compute costs, and enable natural multi-speaker interactions. Especially valuable for robotics, smart home devices, and XR headsets where on-device processing without a GPU is critical.

Common actions

route device-directed speech

detect addressee

reduce ASR compute

enable multi-speaker interaction

deploy voice AI on-device

Capabilities

Key features

Pre-ASR voice routing
Addressee detection
On-device inference
No GPU required
Low latency (<150ms)
Small runtime footprint (<20MB)
Python, Java, C++ SDK

Target Audience

developersproduct managersai engineers

Integrations

Not yet documented

Pricing & Plans

Custom · Likely Not Free

Not publicly disclosed. Check attentionlabs.ai for current pricing.

FAQs

What is Selective Attention System (SAS) by Attention Labs?

SAS is an on-device pre-ASR selective auditory attention SDK that classifies incoming audio as silent, talking to a human, or talking to the device. It routes only device-addressed speech to your ASR pipeline, solving the cocktail party problem for voice AI.

Does Selective Attention System (SAS) require a GPU?

No, SAS runs entirely on ARM Cortex-A class CPUs without requiring a GPU or NPU. It is designed for constrained edge hardware, with a runtime footprint under 20MB, making it suitable for embedded Linux systems and robotics platforms.

How does SAS compare to traditional wake words?

Unlike wake-word systems that require a trigger phrase, SAS determines device-directed speech without any specific phrase, using acoustic geometry and interaction-state estimation. This enables more natural multi-speaker voice interaction, though it can also pair with wake words for higher precision.

What platforms and programming languages does SAS support?

SAS supports Python, Java, and C++ APIs. It runs on Linux, Android, Windows, macOS, Raspberry Pi, and ARM Cortex-A class embedded hardware, including robotics platforms, smart home devices, and XR headsets.

What are the performance metrics for SAS?

SAS achieves 95% F1 with audio+video and 86% F1 with audio-only in real-world scenarios. It has a decision latency under 150ms on ARM Cortex-A72 hardware and a runtime footprint under 20MB.

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