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.

A binaural speech enhancement algorithm that combines superdirective beamforming with time-frequency (TF) masking is proposed. Supervised machine learning is used to design a speech/noise classifier that estimates the ideal binary mask (IBM), which is further softened to reduce musical noise. The method is energy-efficient in two ways: the computational complexity is limited and the wireless data transmission optimized. The experimental work demonstrates the ability of the method to increase the intelligibility of speech corrupted by different types of noise in low SNR scenarios.

Indoor localization of multiple speech sources in wireless acoustic sensor networks (WASNs) is an open and interesting problem with many practical applications, but the presence of noise and reverberations complicates the problem. In this paper, a distributed algorithm for multiple DOA estimation of speech sources in WASNs is presented. The method exploits the sparsity of speech sources in the time-frequency domain to obtain DOA estimations locally in each node of the network. The DOA estimations of different nodes are further combined to increase the accuracy of the local DOA estimations. Since the local DOAs are estimated using only the microphones of the same node, the synchronization between input channels and localization of the microphones from different nodes are not an issue.

The solution of speech related problems such as source location or separation relies on a prior estimation of the number of sources. In this paper we propose a method for speech source enumeration based on the different relative delays that sources at different locations register at two microphones. The Probability Density Function (PDF) of the estimated delays exhibits peaks associated with each source. The Minimum Description Length (MDL) criterion is applied to the prediction error of a linear model fitted to the delay estimates. The method is validated for the estimation of different number of sources and different mixtures.

Intelligent vehicles provide several speech-based advanced features, which success rely on the robustness of the speech acquired by the vehicle microphone system. This speech can be enhanced with a microphone array by means of spatial filtering, which design needs a reliable noise model to guarantee good filtering performance. Traditionally, it has been assumed that this noise is diffuse, but different measurements in real scenarios revel that the coherence of the noise is high to consider that is completely diffuse. In this paper, we suggest that the major contribution of the noise in a car can be reduced to a finite number of uncorrelated noise sources. We propose a searching algorithm to identify the position of the most uncorrelated sources, obtaining also their relative energy. This model allows to generate reliable synthetic noise based on real measurements, which can be very useful in the design of spatial filters.