  Deep-neural-network (DNN) based noise suppression systems yield significant
improvements over conventional approaches such as spectral subtraction and
non-negative matrix factorization, but do not generalize well to noise
conditions they were not trained for. In comparison to DNNs, humans show
remarkable noise suppression capabilities that yield successful speech
intelligibility under various adverse listening conditions and negative
signal-to-noise ratios (SNRs). Motivated by the excellent human performance,
this paper explores whether numerical models that simulate human cochlear
signal processing can be combined with DNNs to improve the robustness of DNN
based noise suppression systems. Five cochlear models were coupled to
fully-connected and recurrent NN-based noise suppression systems and were
trained and evaluated for a variety of noise conditions using objective
metrics: perceptual speech quality (PESQ), segmental SNR and cepstral distance.
The simulations show that biophysically-inspired cochlear models improve the
generalizability of DNN-based noise suppression systems for unseen noise and
negative SNRs. This approach thus leads to robust noise suppression systems
that are less sensitive to the noise type and noise level. Because cochlear
models capture the intrinsic nonlinearities and dynamics of peripheral auditory
processing, it is shown here that accounting for their deterministic signal
processing improves machine hearing and avoids overtraining of multi-layer
DNNs. We hence conclude that machines hear better when realistic cochlear
models are used at the input of DNNs.
