Speech signals convey various pieces of information such as the identity of its speaker, the language spoken, and the linguistic information about the text being spoken, etc. In this paper, we extract information about the cell phones from their speech records by using mel-frequency cepstrum coefficients and identify their brands and models. Closed-set identification rates of 92.56% and 96.42% have been obtained on a set of 14 different cell phones in the experiments using vector quantization and support vector machine classifiers, respectively.

Trellis coded multi-h CPM schemes have been shown in the literature to provide power gains, but at the expense of increased receiver complexity and a slight bandwidth expansion in some cases. However, in this paper, a trellis coding combined with self-synchronous multi-h CPM technique is described which does not require the additional multi-h synchronisation overhead and maximises the minimum Euclidean distance with the potential of improved spectrum. This is achieved by making the multi-h pattern be associated with the encoder state transitions, and thus repeating it rather than cyclically changing in time for successive symbol intervals. This results in a simpler receiver with better performance. The scheme has also the advantage of maximising the time diversity.