Abstract This paper analyses the performance of different types of Deep Neural Networks to jointly estimate age and identify gender from speech, to be applied in Interactive Voice Response systems available in call centres. Deep Neural Networks are used, because they have recently demonstrated discriminative and representation capabilities in a wide range of applications, including speech processing problems based on feature extraction and selection. Networks with different sizes are analysed to obtain information on how performance depends on the network architecture and the number of free parameters. The speech corpus used for the experiments is Mozilla’s Common Voice dataset, an open and crowdsourced speech corpus. The results are really good for gender classification, independently of the type of neural network, but improve with the network size. Regarding the classification by age groups, the combination of convolutional neural networks and temporal neural networks seems to be the best option among the analysed, and again, the larger the size of the network, the better the results. The results are promising for use in IVR systems, with the best systems achieving a gender identification error of less than 2% and a classification error by age group of less than 20%.

Gunshot acoustic analysis is a field with many practical applications, but due to the multitude of factors involved in the generation of the acoustic signature of firearms, it is not a trivial task. The main problem arises with the strong spatial dependence shown by the recorded waveforms even when dealing with the same weapon. However, this can be lessen by using a spatially diverse receiver such as a wireless acoustic sensor network. In this work, we address multichannel acoustic weapon classification using spatial information and a novel decision fusion rule based on it. We propose a fusion rule based on maximum likelihood estimation that takes advantage of diverse classifier ensembles to improve upon classic decision fusion techniques. Classifier diversity comes from a spatial segmentation that is performed locally at each node. The same segmentation is also used to improve the accuracy of the local classification by means of a divide and conquer approach.

We propose a new acoustic self-localization and orientation estimation algorithm for smartphones networks composed of commercial off-the-shelf devices equipped with two microphones and a speaker. Each smartphone acts as an acoustic transceiver, which emits and receives acoustic signals. Node locations are found by combining estimates of the range and direction of arrival (DoA) between node pairs using a maximum likelihood (ML) estimator. A tailored optimization algorithm is proposed to simultaneously solve the DoA uncertainty problem that arises from the use of only 2 microphones per node and obtain the azimuthal orientation of each node without requiring an electronic compass.