umik-base-app 0.10.0

Base utilities for working with the Umik-1 microphone in Python applications.

🎤 Audio Measurement Framework and CLI

A Python framework and CLI toolkit for real-time audio measurement with USB microphones.

• 📦 Use as a library to build custom real-time audio measurement applications
• 🕹️ Use the bundled CLI (audio-tools) for out-of-the-box measurement, recording and real-time spectrum analyzer (RTA)

Works with USB measurement microphones out of the box — auto-detects known devices, validates sample rate support, and applies per-unit calibration files.

PyPI package: umik-base-app (name reflects the project's origins; a rename is planned as the framework scope expands)

pip install umik-base-app

🎛️ Verified Hardware

The framework works with any USB audio input device. The following microphones have built-in device profiles and calibration support:

Microphone	Manufacturer	Sample Rates	Sensitivity
UMIK-1	miniDSP	48 kHz	−18 dBFS
UMIK-2	miniDSP	44.1–192 kHz	−18 dBFS
UMM-6	Dayton Audio	48 kHz	−18 dBFS
XREF 20	Sonarworks	48 kHz	−26 dBFS
MM 1	Beyerdynamic	44.1–192 kHz	−40 dBFS
M23/M30	Earthworks	44.1–192 kHz	−36 dBFS

Custom device profiles can be added — see docs/FRAMEWORK.md.

⚡ Quick Start — Library

Subclass AudioSink, wire it into an AudioPipeline, and let AudioBaseApp handle device management, threading, reconnection, and calibration injection:

from umik_base_app import AppArgs, AudioBaseApp, AudioPipeline, AudioSink, PipelineContext

class LoudnessPrinter(AudioSink):
    def handle(self, ctx: PipelineContext) -> None:
        if ctx.can_calculate_dbspl():
            print(f"[{ctx.timestamp}] dBSPL: {ctx.reference_dbspl:.1f}")

def main():
    args = AppArgs.get_args()
    config = AppArgs.validate_args(args)

    pipeline = AudioPipeline(sample_rate=config.sample_rate)
    pipeline.add_sink(LoudnessPrinter())

    # If --calibration-file was passed, CalibratorAdapter is auto-injected
    app = AudioBaseApp(app_config=config, pipeline=pipeline)
    app.run()

if __name__ == "__main__":
    main()

Run it with any standard flags:

python my_app.py --calibration-file "umik-1/7175488.txt"
python my_app.py --producer --zmq-port 5555   # daemon / distributed mode

🏗️ Framework Concepts

Class	Role
AudioSink	Protocol — implement handle(ctx) to consume audio (metrics, recording, UI)
AudioTransformer	Protocol — implement apply(ctx) to modify audio (filtering, gain)
PipelineContext	Per-chunk envelope: audio samples, timestamp, calibration metadata
AudioPipeline	Ordered transformer chain + fan-out sink execution
AudioBaseApp	Lifecycle manager: device init, threads, transport, reconnection
AudioMetrics	Static helpers: dBFS, dBSPL, RMS, LUFS, spectral_flux

Full API reference and examples: docs/FRAMEWORK.md

🕹️ Bundled Sample Apps (CLI)

The package ships a set of reference applications accessible via audio-tools. These demonstrate the framework and are ready to use out of the box:

Command	Description
audio-tools --devices	🔍 List available audio input devices
audio-tools --meter	📊 Real-time SPL / LUFS / dBFS meter
audio-tools --record	🎙️ Calibrated audio recorder (WAV)
audio-tools --calibrate	🔧 Generate FIR filter from a calibration file
audio-tools --analyze	🔬 Analyze a WAV file and export metrics to CSV
audio-tools --plot	📈 Plot a metrics CSV as a chart
audio-tools --batch	📁 Batch-analyze a directory of WAV files
audio-tools --enhance	✨ Filter and enhance voice audio
audio-tools --convert	🔄 Convert WAV recordings to OGG / MP3 / AAC
audio-tools --clip	✂️ Trim a WAV file to a time range (CLI)
audio-tools-spectrum	🌐 Browser-based real-time spectrum analyzer (RTA)
audio-tools-clip	🎛️ Browser-based waveform clip editor

🌐 Spectrum Analyzer

A browser-based Real-Time Analyzer (RTA) — comparable to the RTA panel in REW — with FFT plot, scrolling waterfall, time-series dBSPL/dBFS, noise floor baseline, and in-browser calibration loading. Opens automatically at http://localhost:8767.

audio-tools-spectrum --device <id> --calibration-file "umik-1/7175488.txt"

✂️ Audio Clip Editor

Trim WAV recordings to clean segments — visually or from the command line. Built for curating acoustic event recordings (dog barks, alarms) into focused training examples for MFCC classifiers.

CLI — scriptable, no server:

# Trim seconds 4–7; output → recordings/clips/bark_4s_7s.wav
audio-clip recordings/bark.wav --start 4 --end 7

# Trim by duration, resample output
audio-clip recordings/bark.wav --start 4 --duration 3 --sr 22050

Browser UI — waveform view with draggable handles, audio preview, one-click save:

# Pre-load a file; browser opens at http://localhost:8768
audio-tools-clip recordings/bark.wav

Full CLI reference: docs/CLI.md

📦 Installation

pip (recommended)

pip install umik-base-app

Requires system libraries:

# Linux (Debian/Ubuntu)
sudo apt install libportaudio2 libsndfile1 ffmpeg libzmq3-dev -y

# macOS
brew install portaudio libsndfile zeromq ffmpeg

APT — Linux headless deployment

For Raspberry Pi and Ubuntu servers, a .deb package installs system dependencies automatically:

curl -fsSL "https://br-se1.magaluobjects.com/audio-tools/audio-tools/pubkey.gpg" \
  | sudo gpg --dearmor -o /usr/share/keyrings/audio-tools.gpg
echo "deb [signed-by=/usr/share/keyrings/audio-tools.gpg] https://br-se1.magaluobjects.com/audio-tools/audio-tools $(lsb_release -cs) main" \
  | sudo tee /etc/apt/sources.list.d/audio-tools.list
sudo apt-get update && sudo apt-get install audio-tools

🍓 Raspberry Pi 4B verified. Suitable for headless acoustic monitoring boxes.

🔗 Related Projects

py-edge-ai-acoustic-monitoring-app — adds ML sound classification (chainsaws, glass breaking, birds) on embedded devices, built on top of this framework.

📚 Documentation

Doc	Description
docs/FRAMEWORK.md	Library API, custom sinks/transformers, device profiles
docs/CLI.md	Full CLI reference, run modes, calibration, TUI, spectrum analyzer
docs/ARCHITECTURE.md	Producer-Consumer design, transport layer, pipeline internals
docs/METRICS.md	RMS, LUFS, dBFS, dBSPL formulas explained
docs/UMIK-Series.md	Hardware-specific calibration details
CONTRIBUTING.md	Dev setup, testing, CI workflow