The Cocktail Party Problem: Speech/Data Signal Separation Comparison between Backpropagation and SONN

Parallel Distributed Structures Laboratory School of Electrical Engineering Purdue University W. Lafayette, IN. 47907 ChristophSchaefers ABSTRACT This work introduces a new method called Self Organizing Neural Network (SONN) algorithm and compares its performance with Back Propagation in a signal separation application. The problem is to separate two signals; a modem data signal and a male speech signal, added and transmitted through a 4 khz channel. The signals are sampled at8 khz, and using supervised learning, an attempt is made to reconstruct them. The SONN is an algorithm that constructs its own network topology during training, which is shown to be much smaller than the BP network, faster to trained, and free from the trial-anderror networkdesign that characterize BP. 1. INTRODUCTION The research in Neural Networks has witnessed major changes in algorithm design focus, motivated by the limitations perceived in the algorithms available at the time. With the extensive work performed in that last few years using multilayered networks, it was soon discovered that these networks present limitations in tasks The Cocktail Party Problem: 543 that: (a) are difficult to determine problem complexity a priori, and thus design network of the correct size, (b) training not only takes prohibitively long times, but requires a large number of samples as well as fine parameter adjustment, without guarantee of convergence, (c) such networks do not handle the system identification task efficiently for systems whose time varying structure changes radically, and, (d) the trained network is little more than a black box of weights and connections, revealing little about the problem structure; being hard to find the justification for the algorithm weight choice, or an explanation for the output decisions based on an input vector.