Combination of Growing and Pruning Algorithms for Multilayer Perceptrons for Speech/Music/Noise Classification in Digital Hearing Aids
This paper explores the feasibility of combining both growing and pruning algorithms in some way that the global approach results in finding a smaller multilayer perceptron (MLP) in terms of network size, which enhances the speech/music/noise classification performance in digital hearing aids, with the added bonus of demanding a lower number of hidden neurons, and consequently, lower computational cost. With this in mind, the paper will focus on the design of an approach that starts adding neurons to an initial small MLP until the stopping criteria for the growing stage is reached. Then, the MLP size is reduced by successively pruning the least significant hidden neurons while maintaining a continuous decreasing function. The results obtained with the proposed approach will be compared with those obtained when using both growing and pruning algorithms separately.
Click to purchase paper or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!
This paper costs $33 for non-members, $5 for AES members and is free for E-Library subscribers.