Exploiting Deep Neural Networks for Two-to-Five Channel Surround Decoder

Abstract

We exploited deep neural networks (DNN) for two-to-five channel surround decoding. Specifically DNNs are used to replace the primary-ambient separation and ambient-signal rendering modules. For the training, the mean-squared error of the magnitude spectra between the decoded and five-channel target signals and the interchannel level differences between the target signals were used as the loss functions. Through this procedure the DNNs can derive the spectral weights that can be used to produce the decoded signals, similar to that for the target signals. The log spectral distance, signal-to-distortion ratio, and multiple stimuli with hidden reference and anchor tests were used for objective and subjective evaluations. The experimental results show that exploiting the DNNs can generate decoded signals that are more similar to the target signals than those obtained via previous methods.