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We show that a Modular Neural Network (MNN) can combine various speechenhancement modules, each of which is a Deep Neural Network (DNN) specializedon a particular enhancement job. Differently from an ordinary ensembletechnique that averages variations in models, the propose MNN selects the bestmodule for the unseen test signal to produce a greedy ensemble. We see this asCollaborative Deep Learning (CDL), because it can reuse various already-trainedDNN models without any further refining. In the proposed MNN selecting the bestmodule during run time is challenging. To this end, we employ a speechAutoEncoder (AE) as an arbitrator, whose input and output are trained to be assimilar as possible if its input is clean speech. Therefore, the AE can gaugethe quality of the module-specific denoised result by seeing its AEreconstruction error, e.g. low error means that the module output is similar toclean speech. We propose an MNN structure with various modules that arespecialized on dealing with a specific noise type, gender, and inputSignal-to-Noise Ratio (SNR) value, and empirically prove that it almost alwaysworks better than an arbitrarily chosen DNN module and sometimes as good as anoracle result.

Collaborative Deep Learning for Speech Enhancement: A Run-Time Model Selection Method Using Autoencoders