A Modular Deep Denoising Autoencoder for speech enhancement

Recent researches have proven that deep denoising autoencoder is an effective method for noise reduction and speech enhancement, and can provide better performance than several existing methods. However, training deep denoising autoencoder has proven to be difficult computationally. The goal of this study is to develop a modular approach for training deep denoising autoencoders as a set of classifiers based on collaborative learning. Each classifier is a multilayer deep denoising autoencoder network and specialized for a particular enhancement task and handles a subset of the complete training dataset. The approach performance was assessed using a perceptual evaluation of speech quality and the segmental signal-to-noise ratio. We have trained two individual DDAEs with three and five hidden layers respectively for comparison purposes. We have also compared our proposed model with the traditional spectral subtraction and, log MMSE methods. The results showed that DDAE with three and five hidden layers was sufficient (deeper is better), while the proposed approach provided higher intelligibility results and was more suitable for high-quality cases.