Modern vehicles, robots, and smart devices often rely solely on cameras, yet many critical events are audible before they are visible. Bolom Acoustic Intelligence gives machines the power to hear, understand, and react - enhancing safety, awareness, and autonomy in real-world environments.

Firetruck emergency vehicle Glass breaking Robot responding to sound
Safer Through Sound
Dog barking Person coughing Storm

Bolom Acoustic Intelligence combines real-time Acoustic Event Detection (AED) and Acoustic Scene Analysis (ASA) in a single, edge-optimized system. It runs on low-power embedded hardware - delivering intelligent sound recognition and situational awareness across automotive, robotics, and IoT environments.

Acoustic Event Detection

A real-time edge AI engine that identifies hundreds of distinct sound events - sirens, horns, alarms, pedestrians, and more - with up to 97% accuracy, even in challenging acoustic environments.

Firetruck emergency vehicle

Acoustic Scene Analysis

An AI-powered scene analysis engine that converts ambient sound into real-time text descriptions - giving intelligent systems the contextual awareness to anticipate hazards, adapt behavior, and respond to complex soundscapes.

Road work scene illustration

How It Works

Audio captured by the device microphone is simultaneously processed by AED for instantaneous acoustic event detection and by ASA for contextual scene understanding. The system runs fully on-device for ultra-low-latency applications, with optional cloud deployment.

Bolom system architecture diagram

Demo

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Specifications

AI Scalability

  • Three Deployment Tiers
    • Base, Small, and Tiny model sizes optimized for performance, power, and memory
  • Multiclass detection
    • Doesn't miss a beat, simultaneously classifying hundreds of distinct sounds in real-time, right at the edge
  • Label Subset Support
    • Easily manage and customize label sets for your specific use case
  • Variable Context Window
    • 2-30 seconds configurable for latency vs. accuracy trade-offs

Performance

  • Up to 97% predictive accuracy in internal real-time benchmarks
  • Our real-time streaming Base model achieves performance comparable to BEATs (a leading audio foundation model)
  • Tiny Model outperforms YAMNet at approximately 50% smaller model size

Integration

  • Our acoustic AI software is processor-agnostic and can be rapidly integrated into any edge SoC or embedded system of choice
    • x86, ARM Cortex-A, Tensilica HiFi DSP, and others
  • For automotive applications, we support external and internal cabin microphones
  • Frameworks:
    • Native Audio Weaver integration enables seamless development workflows
    • Tymphany Flow DSP integration a drag-and-drop design environment
    • Pipecat integration for voice and multimodal conversational AI
    • Standalone SDK
    • Other framework integrations coming soon.

Interested in integrating Bolom into your product?
Get in touch.

info@bolom.com
www.bolom.com