acoustic-engine 1.2.0

Real-time acoustic alarm pattern detection engine

Acoustic Engine

Lightweight, Deterministic Sound Recognition for IoT

A high-performance, noise-resilient DSP library designed to detect specific acoustic patterns (Smoke Alarms, CO Detectors, Appliance Beeps) on lightweight hardware.

[!TIP] Why this engine? Unlike heavy Neural Networks, the Acoustic Engine uses deterministic Digital Signal Processing (DSP) to achieve high accuracy with low CPU usage. Best for battery-powered or resource-constrained IoT devices.

[!NOTE] New here? → Getting Started takes you from install to a working detector in about ten minutes, no signal-processing knowledge needed.

Features

• Installs clean on a laptop: pip install acoustic-engine and go — PortAudio is bundled in the macOS/Windows wheels (Linux: one apt line). No compilers, no heavy ML stack.
• Zero-config presets: ship-ready profiles for standardized smoke (T3) and CO (T4) alarms — acoustic-engine run --preset smoke_t3.
• Learn from a recording: acoustic-engine learn alarm.wav turns a clip of your alarm into a working profile automatically — no DSP knowledge required.
• One CLI: run, learn, test, profiles, serve, plus devices/doctor to confirm your mic works in seconds — reference.
• Act on detections: publish MQTT, POST a webhook, or run any shell command (--on-detect) — turn a laptop into an alarm-watching agent.
• Noise Resilient: event analysis that ignores background interference (down to ~-15 dB SNR).
• Efficient: real-time FFT with low CPU usage — runs comfortably on a Raspberry Pi.
• Easy config: simple YAML profiles, validated on load with clear error messages.

---

Quick Start

Installation

pip install acoustic-engine

# …or the very latest from git
pip install "git+https://github.com/h0tp-ftw/acoustic-engine.git"

Audio capture uses sounddevice, whose macOS and Windows wheels bundle PortAudio — so on a laptop there is nothing else to install. On Linux, add the small PortAudio runtime once:

sudo apt install libportaudio2     # Debian / Ubuntu / Raspberry Pi OS

For development, clone with the optional extras (mqtt = publish detections, tuner = browser app API, dev = tests):

git clone https://github.com/h0tp-ftw/acoustic-engine.git
cd acoustic-engine
pip install -e ".[mqtt,tuner,dev]"

Quick Start (CLI)

Everything is behind one acoustic-engine command. The common cases need no DSP knowledge and no config file:

# 0. Is my mic working? (lists inputs, then a 5s live level meter)
acoustic-engine devices
acoustic-engine doctor

# 1. What can I detect out of the box?
acoustic-engine profiles

# 2. Listen for a standard smoke alarm (ISO 8201 T3) — zero config
acoustic-engine run --preset smoke_t3

# 3. Have a recording of YOUR alarm? Turn it into a profile automatically
acoustic-engine learn my_alarm.wav --name "My Dryer"      # writes my_alarm.yaml
acoustic-engine test --profile my_alarm.yaml --audio my_alarm.wav -v   # verify it
acoustic-engine run --profile my_alarm.yaml               # deploy it

# 4. Make it an agent — do something when it fires (no broker needed)
acoustic-engine run --preset smoke_t3 --on-detect 'notify-send "Alarm: {name}"'
acoustic-engine run --preset smoke_t3 --webhook https://ntfy.sh/my-alarms

# 5. Production: run from a config file (multiple alarms, MQTT, tuning)
acoustic-engine run --config config.example.yaml

The three tiers: presets (zero config) → learn + hand-edit the YAML shape (frequencies/durations, no DSP) → advanced engine tuning (you rarely need this). You only go as deep as your sound requires.

Docker

The mic is shared into the container via /dev/snd. See deploy/README.md for systemd and Docker details.

docker compose up engine          # run detection
docker compose run --rm engine acoustic-engine run --preset smoke_t3

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Documentation

Guide	What's in it
Getting Started	Install → detect → learn your own alarm → deploy. Start here.
CLI Reference	Every command and option.
Profiles & Troubleshooting	How profiles work, and fixes for "won't detect" / "false alarms".
Deployment	systemd, Docker, hardware tips.
Tuning Guide	The advanced engine knobs (rarely needed).
Architecture	How the pipeline works inside.
Changelog	What's new in each release.

---

Profile Tuner (Browser App)

The project includes a React-based browser tool for building alarm YAML configs from audio recordings.

cd tuner
npm install
npm run dev
# Open http://localhost:5173

Features:

• FFT-based frequency estimation with parabolic interpolation (~10Hz accuracy)
• Adaptive threshold (noise-floor relative) with spectral gating
• Auto cycle detection — finds repeating patterns without manual input
• YAML import/export matching the engine's profile schema
• Crop, playback, spectrogram visualization

Validate Against the Real Engine

The tuner includes a validation API that runs your audio + profile through the actual detection pipeline (SpectralMonitor, FrequencyFilter, EventGenerator, WindowedMatcher) so you know with certainty whether the engine will detect your alarm.

# Terminal 1: Start the validation API
acoustic-engine serve --port 8787

# Terminal 2: Start the tuner
cd tuner && npm run dev

Click "Validate with Real Engine" in the browser. Engine tone events appear as a cyan overlay on the timeline alongside the browser's own analysis, so you can compare both.

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Use Cases

• Life Safety: Industry-standard Smoke (T3) and CO (T4) alarm detection.
• Appliances: Detect microwave beeps, oven timers, or dishwasher completion chimes.
• Medical: Monitor critical patient equipment alarms in noisy hospitals.
• Industrial: Identify specific machinery fault codes or safety buzzers in high-reverb warehouses.
• Smart Home: Trigger automation when your doorbell or dryer buzzes.

Not Suited For

• Single / Lone Beeps: A single beep is too generic. The engine relies on repetition (rhythm) for specificity.
• Complex Non-Tonal Sounds: Dog barks, glass breaking, or speech. Use a Neural Network for these.
• Variable Melodies: Tunes that change notes every time.

---

Alarm Profiles

Define patterns in YAML. One sound = one profile.

name: "SmokeAlarm_T3"
confirmation_cycles: 2

segments:
  - type: "tone"
    frequency: { min: 3150, max: 3350 }
    duration: { min: 0.5, max: 0.7 }
  - type: "silence"
    duration: { min: 0.25, max: 0.5 }
  - type: "tone"
    frequency: { min: 3150, max: 3350 }
    duration: { min: 0.5, max: 0.7 }
  - type: "silence"
    duration: { min: 1.0, max: 3.0 }

# Optional per-profile resolution override
resolution:
  min_tone_duration: 0.05
  dropout_tolerance: 0.05

Optional Parameters

reset_timeout: 10.0      # Seconds of silence before resetting match progress
window_duration: 10.0     # Analysis window size (auto-calculated if omitted)
eval_frequency: 0.5       # How often to evaluate windows

---

Testing Profiles

Against Audio Files

python -m acoustic_engine.tester \
  --profile profiles/smoke_alarm.yaml \
  --audio recording.wav \
  -v

Live Microphone

python -m acoustic_engine.tester \
  --profile profiles/ \
  --live \
  --duration 60

With Noise Mixing

python -m acoustic_engine.tester \
  --profile profiles/smoke_alarm.yaml \
  --audio recording.wav \
  --noise 0.3 \
  --noise-type white

Noise types: white, pink, brown

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Robustness & Benchmarks

Performance Metrics

• Extreme Noise Resilience: Confirmed detection at -15dB SNR (White/Pink Noise).
• Spectral Subtraction: Per-bin noise profiling learns and subtracts stationary noise (fans, HVAC, motors).
• Dynamic Background Rejection: Identifies alarms even in "Cocktail Party" scenarios at negative SNR.
• Echo/Reverb Rejection: Dip-disconnect logic handles reverb decays of up to 50%.
• Frequency Drift Tracking: Follows dying piezo buzzers sweeping up to 200Hz without losing lock.
• Alarm Collision Isolation: Detects target alarm while a louder distractor alarm sounds in a different frequency lane.
• Absolute Specificity: Zero false positives against imposter timers with similar but incorrect rhythms.

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DSP vs Neural Networks

Metric	Acoustic Engine (DSP)	Neural Network (Edge AI)
CPU	3-5% (Pi 4)	25-80%
Latency	~50ms	200ms+
Determinism	100% (Math)	Probabilistic
Data Needed	None (Zero-shot)	Thousands of samples

Data is needed for configuration file generation, but not for runtime.

Use this engine for specific, repetitive patterns (alarms, beeps, machinery). Use Neural Networks for general semantic sounds (shouting, glass breaking, dog barking).

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Architecture

graph TD
    subgraph "Input Layer"
        MIC[Microphone] --> AL[AudioListener]
        FILE[Audio File] --> PR[Manual Processing]
    end

    subgraph "Processing Layer"
        AL --> SM[SpectralMonitor<br/>FFT + Peak Detection]
        PR --> SM
        SM --> FF[FrequencyFilter<br/>Noise Screener]
    end

    subgraph "Analysis Layer"
        FF --> EG[EventGenerator<br/>Peaks to Tones]
        EG --> WM[WindowedMatcher<br/>Pattern Matching]
    end

    subgraph "Output Layer"
        WM --> CB[Callbacks / Detections]
    end

    subgraph "Configuration"
        YP[YAML Profiles] --> WM
        CONFIG[GlobalConfig] --> AL
        CONFIG --> SM
    end

• Input: Hardware-agnostic. sounddevice for live capture (PyAudio fallback), or process_chunk() for any source.
• Processing: SpectralMonitor (FFT + adaptive noise floor) then FrequencyFilter (discards irrelevant frequencies).
• Analysis: EventGenerator (debounces peaks into tone events) then WindowedMatcher (sliding window pattern matching).

See ARCHITECTURE.md for implementation details.

---

Configuration

system:
  log_level: "INFO"

audio:
  sample_rate: 44100
  chunk_size: 1024

engine:
  min_magnitude: 10.0
  min_sharpness: 1.5
  noise_floor_factor: 3.0
  frequency_tolerance: 50.0
  dip_threshold: 0.6

profiles:
  - include: "profiles/smoke_alarm.yaml"

Parameter Reference

Category	Parameter	Default	Description
DSP	min_sharpness	1.5	Ratio a peak must be above its neighbors to be considered a tone.
DSP	noise_floor_factor	3.0	Multiplier for the adaptive noise floor threshold.
Gen	dip_threshold	0.6	Detects sudden magnitude drops to disconnect reverb tails.
Gen	freq_smoothing	0.3	Alpha for EMA frequency tracking (higher = faster tracking).
Match	noise_skip_limit	2	Non-matching events to ignore before breaking a cycle.
Match	duration_relax_low	0.8	Multiplier for minimum segment duration during matching.

See docs/tuning_guide.md for scenario-based tuning recipes.

---

Python API

from acoustic_engine import Engine
from acoustic_engine.config import AudioSettings, EngineConfig
from acoustic_engine.profiles import load_profiles_from_yaml

profiles = load_profiles_from_yaml("profiles/smoke_alarm.yaml")

engine = Engine(
    profiles=profiles,
    audio_config=AudioSettings(sample_rate=44100, chunk_size=1024),
    on_detection=lambda name: print(f"ALARM: {name}")
)

engine.start()

Key Classes

• Engine — Main orchestrator. start() for blocking mic capture, process_chunk() for manual feeding.
• ParallelEngine — Runs multiple isolated Engine instances sharing one audio input.
• AlarmProfile — Pattern definition: name, segments, confirmation_cycles, optional resolution.
• AudioSettings — Audio capture config: sample_rate, chunk_size, device_index, channels.
• EngineConfig — Pipeline tuning: magnitudes, tolerances, thresholds. Auto-computed from profiles via EngineConfig.from_profiles().

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CLI Tools

Command	Purpose
python -m acoustic_engine.runner --config <yaml>	Production runner (multi-config, parallel pipelines)
python -m acoustic_engine.tester --profile <yaml> --audio <file>	Test profiles against audio files
python -m acoustic_engine.tester --profile <yaml> --live	Live microphone testing
python -m acoustic_engine.tuner.validate --port 8787	Validation API for the browser tuner

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Resource Consumption

Measured on a standard Linux workstation (x86_64):

Resource	Idle	Active (Listening)	Active (Detection)
RAM	~37 MB	~40 MB	~43 MB
CPU (1 Core)	< 1%	< 1%	< 1%

Requirements

• Python 3.9+
• System Dependencies: none on macOS/Windows; on Linux, libportaudio2 (sudo apt install libportaudio2) for microphone access.
• Python Libraries: numpy, sounddevice, PyYAML (installed automatically).

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Development

pip install -e ".[dev]"
pytest tests/ -v

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License

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).

• Attribution: You must give appropriate credit to @h0tp-ftw on github.com.
• Non-Commercial: You may not use the material for commercial purposes.

See LICENSE for the full text.