CLC: Complex Linear Coding for the DNS 2020 Challenge

Schröter H, Rosenkranz T, Escalante Banuelos A, Maier A (2020)

Publication Language: English

Publication Status: Published

Publication Type: Other publication type

Future Publication Type: Other publication type

Publication year: 2020


Open Access Link:


Complex-valued processing brought deep learning-based speech enhancement and signal extraction to a new level. Typically, the noise reduction process is based on a time-frequency (TF) mask which is applied to a noisy spectrogram. Complex masks (CM) usually outperform real-valued masks due to their ability to modify the phase. Recent work proposed to use a complex linear combination of coefficients called complex linear coding (CLC) instead of a point-wise multiplication with a mask. This allows to incorporate information from previous and optionally future time steps which results in superior performance over mask-based enhancement for certain noise conditions. In fact, the linear combination enables to model quasi-steady properties like the spectrum within a frequency band. In this work, we apply CLC to the Deep Noise Suppression (DNS) challenge and propose CLC as an alternative to traditional mask-based processing, e.g. used by the baseline.

We evaluated our models using the provided test set and an additional validation set with real-world stationary and non-stationary noises. Based on the published test set, we outperform the baseline w.r.t. the scale independent signal distortion ratio (SI-SDR) by about 3dB.

Authors with CRIS profile

Additional Organisation(s)

Related research project(s)

Involved external institutions

How to cite


Schröter, H., Rosenkranz, T., Escalante Banuelos, A., & Maier, A. (2020). CLC: Complex Linear Coding for the DNS 2020 Challenge.


Schröter, Hendrik, et al. CLC: Complex Linear Coding for the DNS 2020 Challenge. 2020.

BibTeX: Download