pyFDN

Overview

pyFDN provides building blocks for designing, simulating, and analysing Feedback Delay Networks (FDNs). The package focuses on reusable, tested helper functions that simplify typical FDN workflows such as creating orthogonal feedback matrices, designing loop filters, and inspecting pole locations. Using flamo as a dependency, pyFDN allows modular design of advanced FDN structure with DSP operations in time and frequency domain.

The pyFDN tutorial provides an introduction to the package.

The source code is MIT-licensed. Small packaged tutorial resources retain the separate terms recorded in src/pyFDN/resources/ATTRIBUTION.md; in particular, the Pori concert-hall impulse response is limited to noncommercial use.

Highlights

Matrix polynomial helpers for evaluating, differentiating, and convolving FIR/IIR blocks.
Loop analysis utilities including pole boundary estimation and curve bounding checks.
Acoustic absorption design tools that translate RT targets into one-pole loop filters.
Echo density (Abel & Huang 2006) for analysing reverberation and mixing time.
Random orthogonal matrix generation to prototype energy-preserving feedback networks.

Installation

Install the current release from PyPI:

pip install pyFDN

For local development, create a virtual environment and install the package in editable mode together with the optional tooling:

python -m venv .venv
source .venv/bin/activate
pip install -e .

Quick start

All main functions are accessible directly from the top-level pyFDN namespace:

import numpy as np
import pyFDN

fs = 48_000
delays = np.array([331, 347, 359, 373], dtype=int)

# energy-preserving feedback matrix
feedback = pyFDN.random_orthogonal(len(delays))

# one-pole absorption filters targeting RT of 1.2 s at DC and 0.9 s at Nyquist
absorption = pyFDN.decay_to_one_pole(1.2, 0.9, delays, fs)

# convert delay state-space to standard state-space (A_ss, b, c, d)
A_ss, b, c, d = pyFDN.dss_to_ss(delays, feedback)

Alternatively, import specific functions directly:

from pyFDN import decay_to_one_pole, lin_to_db, random_orthogonal

feedback = random_orthogonal(4)
absorption = decay_to_one_pole(1.2, 0.9, [100, 150, 200, 250], 48_000)

Development

Run the test suite (the configuration mirrors CI and emits coverage details):

tox -e py311

Or, inside an activated virtual environment:

pytest --cov=src/pyFDN --cov-report=term-missing

For linting and packaging helpers see Makefile (make lint/make docs) and tox.ini for multi-environment testing.

Helpful links

Development Status
- 3 - Alpha
Intended Audience
- Science/Research
Programming Language

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