PyOctaveBand 2.0.0

Octave-Band and Fractional Octave-Band filter for signals in time domain.

PyOctaveBand

Advanced Octave-Band and Fractional Octave-Band filter bank for signals in the time domain. Fully compliant with ANSI S1.11-2004 (Filters) and IEC 61672-1:2013 (Weighting).

✨ Highlights

• 🎛️ 1/1, 1/3 and arbitrary fractional octave filter banks (stable SOS + multirate decimation)
• 🏗️ Five architectures: Butterworth, Chebyshev I/II, Elliptic, Bessel — all with −3 dB points on the ANSI band edges
• 🔊 A/C/Z frequency weighting within IEC 61672-1 class 1 tolerances
• ⏱️ Fast/Slow/Impulse time ballistics, Leq, LAeq and L10/L50/L90 statistical levels
• 🗺️ Octave spectrogram (band levels over time) and zero-phase offline filtering
• 📏 Physical SPL calibration and dBFS modes
• ⚡ Vectorized multichannel processing and stateful block (real-time) workflows

🚀 Installation

pip install PyOctaveBand

Optional extras: PyOctaveBand[plot] (matplotlib for response plots), PyOctaveBand[perf] (numba for faster impulse ballistics), PyOctaveBand[full] (both).

📚 Documentation

Full documentation website: https://jmrplens.github.io/PyOctaveBand/ (English / Español)

Or browse the Markdown docs on GitHub:

Page	Contents
Getting Started	Installation, first analysis, WAV files
Filter Banks	Architectures, response gallery, band decomposition, zero-phase
Frequency Weighting	A/C/Z curves, class 1 high-accuracy mode
Time Weighting	Fast/Slow/Impulse ballistics, initial state
Levels	Leq, LAeq, L10/L50/L90, octave spectrogram
Calibration and dBFS	Physical SPL, digital full-scale, RMS vs peak
Block Processing	Stateful streaming workflows
Multichannel	Vectorized multichannel analysis, performance
API Reference	Every public function and class
Theory	Standards, math, design decisions
Why PyOctaveBand	IEC compliance verification vs other libraries

⚡ Quick start

import numpy as np
from pyoctaveband import octavefilter

fs = 48000
t = np.linspace(0, 1, fs, endpoint=False)
# Composite signal: 100Hz + 1000Hz
signal = np.sin(2 * np.pi * 100 * t) + np.sin(2 * np.pi * 1000 * t)

# Apply 1/3 octave filter bank
spl, freq = octavefilter(signal, fs=fs, fraction=3)

print(f"Bands: {freq}")
print(f"SPL [dB]: {spl}")

1/3 Octave Band spectrum analysis of a complex signal. More examples in the documentation.

🧪 Development

make install   # dependencies + editable install
make check     # ruff + mypy + bandit + tests
make graphs    # regenerate documentation images

See https://github.com/jmrplens/PyOctaveBand/blob/main/CONTRIBUTING.md and the https://github.com/jmrplens/PyOctaveBand/blob/main/CHANGELOG.md

📄 License

MIT