Getting Started

Installation

Option 1: From PyPI (Recommended)

pip install phonometry

Optional extras:

pip install phonometry[plot]   # matplotlib, for filter response plots
pip install phonometry[perf]  # numba, faster 'impulse' time weighting
pip install phonometry[full]  # both

Option 2: Cloning and Installing

git clone https://github.com/jmrplens/phonometry.git
cd phonometry
pip install .

Option 3: Git Submodule

git submodule add https://github.com/jmrplens/phonometry.git
# Then install in editable mode to use it from your project
pip install -e ./phonometry

The processing chain at a glance

Every phonometry analysis is some subset of one pipeline — take the raw signal, convert it to physical units, weight it in frequency, split it into standardized bands, smooth it in time and reduce it to metrics:

Each stage is an independent function or class you can use on its own; the guides cover them left to right (Calibration → Frequency Weighting → Filter Banks → Time Weighting → Levels).

Basic Usage: 1/3 Octave Analysis

Analyze a signal and get the Sound Pressure Level (SPL) per frequency band.

import numpy as np
from phonometry 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}")

Example of a 1/3 Octave Band spectrum analysis of a complex signal.

Analyzing an audio file

from scipy.io import wavfile
from phonometry import octavefilter

# Load standard WAV file
fs, signal = wavfile.read("measurement.wav")

# Analyze
# Note: To obtain real-world SPL values, you must calibrate the input.
# See the Calibration guide.
spl, freq = octavefilter(signal, fs=fs, fraction=3)

Integer audio (e.g. int16 WAV data) is converted to float64 internally, so it is safe to pass wavfile.read output directly.

Where to go next

• Filter Banks — choose an architecture and inspect responses
• Calibration and dBFS — get real-world SPL values
• API Reference — every parameter of every function