Giving LLM Agents Local Auditory Perception

es617_dev · reddit · 2026-07-16

The author developed an open-source experimental framework to give LLM agents "auditory perception." Beyond just speech-to-text, it can recognize environmental sounds like bird calls, breaking glass, and smoke alarms.

Core Pipeline

Gate: Uses energy, novelty, and Silero VAD to filter continuous audio streams, sending only a few candidate segments down the pipeline.
Fingerprint: Uses CLAP embedding along with dozens of symbolic features (spectral shape, harmonics, peaks, time drift, etc.) for feature representation.
Recognize: Performs top-k similarity search via sqlite-vec. Once matched to a learned concept, it takes a server-side fast path without needing to call an LLM.
Unhandled: Unrecognized content enters a queue for annotation. Once labeled by a teacher, it forms a new concept; the system auto-calibrates thresholds over time.

Current Status & Applications

Currently integrates three types of specialists: Whisper, BirdNET, AST, with support for expanding to more classifiers.
The author hasn't conducted formal benchmarks, only doing lightweight evaluations on personal recordings.
Envisioned applications include: robots perceiving anomalous sounds, home assistants detecting oven timers, and industrial monitoring for early detection of bearing failures.

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Giving LLM Agents Local Auditory Perception

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