Speech/music/noise classification in hearing aids using a two-layer classification system with MSE linear discriminants

This paper focuses on the development of an automatic sound classifier for digital hearing aids that aims to enhance the listening comprehension when the user goes from a sound environment to another different one. The implemented approach consists in dividing the classifying algorithm into two layers that make use of two-class algorithms that work more efficiently: the input signal discriminated by the first layer into either speech or non-speech is ulteriorly classified more specifically depending on whether the audio is noise or music. The complete system results in having three classes, labeled “speech”, “noise” and “music”. The classification process is carried out by using a mean squared error linear discriminant, which provides very good results along with a low computational complexity. This is a crucial issue because hearing aids have to work at very low clock frequency. The paper explores the feasibility of this approach thanks to a number of experiments that prove the advantages of using the proposed two-layer system rather than a three-classes, single-layer classifier.