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

CLCNet: Deep learning-based Noise Reduction for Hearing Aids using Complex Linear Coding