Signal and Noise Generation
ANF Generator — Arbitrary Noise Fields
Generates multi-channel noise signals with a predefined spatial coherence function. Supports spherically isotropic, cylindrically isotropic, and Corcos (wind-noise) coherence models. The mixing matrix is obtained by Cholesky or eigenvalue decomposition; three post-processing methods (smooth, balanced, balanced+smooth) based on the unitary Procrustes solution improve spectral smoothness and mix balance [1]. Suitable for generating babble speech, factory noise, and wind noise in multi-sensor configurations.
The Python implementation is available here and can be installed via pip install anf-generator. The MATLAB implementation is available here.
INF Generator — Isotropic Noise Fields
Generates sensor signals for an arbitrary one- or three-dimensional array that result from a spherically or cylindrically isotropic noise field. Implements the algorithms described in [2] and [3].
The MATLAB implementation is available here.
Wind Noise Generator
Generates synthetic wind noise signals based on a wind speed profile using a physically motivated statistical model. Supports diverse application scenarios including audio signal processing, noise reduction, audio production, game audio, and virtual reality. Each generated sample is statistically independent, enabling realistic and varied simulations well-suited for training deep learning-based wind noise reduction systems. The underlying model is described in [4].
The Python implementation is available here. The MATLAB implementation is available here.
Anechoic Interferer Dataset Generator
A Python utility for generating mixtures of random, anechoic, non-stationary noise signals. Intended for use as interferer signals in speech enhancement and noise suppression experiments. The accompanying dataset as described in [5] is availbale on Zenodo.
The Python implementation is available here.
References
- D. Mirabilii, S.J. Schlecht, E.A.P. Habets, Generating coherence-constrained multisensor signals using balanced mixing and spectrally smooth filters, Journal of the Acoustical Society of America, 149:1425, 2021.
- E.A.P. Habets and S. Gannot, Generating sensor signals in isotropic noise fields, Journal of the Acoustical Society of America, 122(6):3464–3470, 2007.
- E.A.P. Habets and S. Gannot, Comments on `Generating Sensor Signals in Isotropic Noise Fields', internal report, Sept. 2010.
- D. Mirabilii, A. Lodermeyer, F. Czwielong, S. Becker and E.A.P. Habets, Simulating wind noise with airflow speed-dependent characteristics, Proc. International Workshop on Acoustic Signal Enhancement (IWAENC), 2022.
- P. Goetz, C. Tuna, A. Walther and E.A.P. Habets, AID: Open-source anechoic interferer dataset, Proc. International Workshop on Acoustic Signal Enhancement (IWAENC), 2022.