Perceptual audio loss function for deep learning

PESQ, Perceptual Evaluation of Speech Quality [5], and POLQA, Perceptual Objective Listening Quality Assessment [1] , are standards comprising a test methodology for automated assessment of voice quality of speech as experienced by human beings. The predictions of those objective measures should come as close as possible to subjective quality scores as obtained in subjective listening tests, usually, a Mean Opinion Score (MOS) is predicted. Wavenet [6] is a deep neural network originally developed as a deep generative model of raw audio waveforms. Wavenet architecture is based on dilated causal convolutions, which exhibit very large receptive fields. In this short paper we suggest using the Wavenet architecture, in particular its large receptive filed in order to mimic PESQ algorithm. By doing so we can use it as a differentiable loss function for speech enhancement. 1 Problem formulation and related work In statistics, the Mean Squared Error (MSE) or Peak Signal to Noise Ratio (PSNR) of an estimator are widely used objective measures and are good distortion indicators (loss functions) between the estimators output and the size that we want to estimate. those loss functions are used for many reconstruction tasks. However, PSNR and MSE do not have good correlation with reliable subjective methods such as Mean Opinion Score (MOS) obtained from expert listeners. A more suitable speech quality assessment can by achieved by using tests that aim to achieve high correlation with MOS tests such as PEAQ or POLQA. However those algorithms are hard to represent as a differentiable function such as MSE moreover, as opposed to MSE that measures the average