Pitch Based Sound Classification A master’s thesis by

The fact that different sound environments need different sound processing is no secret, but how to select between the different programs is very different from hearing aid to hearing aid. Complete automatic and reliable classification is desirable, because many hearing aid users are not able to select programs themselves. In this project the emphasis is on classification based on the pitch of the signal, and three classes, music, noise and speech, is used. Unfortunately pitch is not straightforward to extract, and the first part of the project is about finding a suitable pitch detector. A new pitch detector is suggested based on two existing algorithms, pattern match with envelope detection and the harmonic product spectrum. The new algorithm is compared to a Bayesian algorithm and HMUSIC, and is found to perform better for classification purposes. Features are extracted from the signal produced by the pitch detector. Apart from the pitch itself, the error from the pitch detector is used to get a measure of how well the extracted pitch describes the signal, i.e. whether the signal is pitched or not. A total o