Method and appartus for audio processing using a nested convolutional neural network architechture

Abstract

Systems, methods, and computer program products for audio processing based on convolutional neural network (CNN) are described. The CNN architecture may comprise a multi-scale input block and a multi-scale nested block. The multi-scale input block may be configured to receive input data and to generate a first downsampled input data set by downsampling the input data. The multi-scale nested block may comprise a first encoding layer configured to generate a first encoded data set by performing a convolution based on the input data. The multi-scale nested block may comprise a second encoding layer configured to generate a second encoded data set by performing a convolution based on the first downsampled input data set. Furthermore, the multi-scale nested block may comprise a first convolutional layer configured to generate a first output data set by upsampling the second encoded data set, concatenating the first encoded data set and the upsampled second encoded data set, and performing a convolution. The first convolutional layer may be nested between the encoding layers and decoding layers, thereby increasing the number of communication channels with the CNN and simplifying the underlying optimization problem.