Clean Audio for TV broadcast: An Object-Based Approach for Hearing-Impaired Viewers - April 2015
Audibility of a CD-Standard A/DA/A Loop Inserted into High-Resolution Audio Playback - September 2007
Sound Board: Food for Thought, Aesthetics in Orchestra Recording - April 2015
On the Training of Multilayer Perceptrons for Speech/Non-Speech Classification in Hearing Aids
This paper explores the application of multilayer perceptrons (MLP) to the problem of speech/non-speech classification in digital hearing aids. When properly designed and trained, MLPs are able to generate an arbitrary classification frontier with a relatively low computational complexity. The paper will focus on studying the key influence of the training process on the performance of the system. An appropriate election of the training algorithm will help to provide better classification with a lower number of neurons in the network, which leads to a lower computational complexity. The results obtained will be compared with those obtained from two reference algorithms (the Fisher linear discriminant and the k-Nearest Neighbour), along with some comments regarding the computational complexity.
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 $20 for non-members, $5 for AES members and is free for E-Library subscribers.