Efficient design of neural networks for the classification of acoustic spectra.

A previous paper by Paul and Nelson [(2021). J. Acoust. Soc. Am. 149(6), 4119-4133] presented the application of the singular value decomposition (SVD) to the weight matrices of multilayer perceptron (MLP) networks as a pruning strategy to remove weight parameters. This work builds on the previous technique and presents a method of reducing the size of a hidden layer by applying a similar SVD algorithm. Results show that by reducing the neurons in the hidden layer, a significant amount of training time is saved compared to the algorithm presented in the previous paper while no or little accuracy is being lost compared to the original MLP model.

Matrix analysis for fast learning of neural networks with application to the classification of acoustic spectra.

Neural networks are increasingly being applied to problems in acoustics and audio signal processing. Large audio datasets are being generated for use in training machine learning algorithms, and the reduction of training times is of increasing relevance. The work presented here begins by reformulating the analysis of the classical multilayer perceptron to show the explicit dependence of network parameters on the properties of the weight matrices in the network. This analysis then allows the application of the singular value decomposition (SVD) to the weight matrices. An algorithm is presented that makes use of regular applications of the SVD to progressively reduce the dimensionality of the network. This results in significant reductions in network training times of up to 50% with very little or no loss in accuracy. The use of the algorithm is demonstrated by applying it to a number of acoustical classification problems that help quantify the extent to which closely related spectra can be distinguished by machine learning.