  Noise reduction is an important part of modern hearing aids and is included
in most commercially available devices. Deep learning-based state-of-the-art
algorithms, however, either do not consider real-time and frequency resolution
constrains or result in poor quality under very noisy conditions. To improve
monaural speech enhancement in noisy environments, we propose CLCNet, a
framework based on complex valued linear coding. First, we define complex
linear coding (CLC) motivated by linear predictive coding (LPC) that is applied
in the complex frequency domain. Second, we propose a framework that
incorporates complex spectrogram input and coefficient output. Third, we define
a parametric normalization for complex valued spectrograms that complies with
low-latency and on-line processing. Our CLCNet was evaluated on a mixture of
the EUROM database and a real-world noise dataset recorded with hearing aids
and compared to traditional real-valued Wiener-Filter gains.
