Coprime array possesses a larger array aperture and element spacing compared with the conventional uniform linear array (ULA) for the equivalent number of sensors, attracting considerable scholarly attention. However, the direction of arrival (DOA) estimation of coherent signals has been a major challenge for the practical application of the coprime array. In this paper, based upon the perspective of signal subspace reconstruction, we propose two effective approaches to resolve the DOA of completely correlated signals with a coprime array. For the first method, we exploit two selection matrices to separate the signal subspace into two parts and rearrange the elements within them to construct two Hankel matrices. By applying the MUSIC method to these Hankel matrices and finding common solutions, we can determine the DOA of coherent signals. In the second method, we first convert the signal subspace of the coprime array into the signal subspace of ULA using a mapping matrix and the total least squares method. We then construct a Hankel matrix and restore its rank by solving a rank minimization problem. Finally, by applying the MUSIC method to the rank-restored Hankel matrix, we can obtain the angles of the coherent signals. Finally, simulation results are presented to demonstrate the efficiency and superiority of our proposed methods.

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%.

A fuzzy rule-based classifier is proposed in this paper where the number of rules in the knowledge base that are fired when an object is classified is anti-monotone with respect to the prior probability of its class. This classifier is intended to secure an equilibrium between accuracy and energy consumption, which is critical in battery operated embedded devices. The method is compared to legacy multi-criteria evolutionary algorithms, where a group of classifiers with different balances between accuracy and consumption are evolved, and the most accurate classifier is selected among those individuals in the Pareto front whose use of the battery does not exceed a given threshold. A significant increase in the battery life is reported without a degradation in the quality of service.

Eco-efficiency in hearing aids is an important issue, related to the maximization of the battery life. In order to minimize the power consumption, the embedded digital signal processor works at very low clock rates, constraining the implementation of signal processing techniques. The implemented algorithms can only use a small number of instructions per second and a small amount of memory. One of the main algorithms implemented in nowadays hearing aids is the voice activity detection algorithm, useful for several noise reduction and speech enhancement algorithms. The objective of this paper is the study of the implementation of voice activity detection algorithms in hearing aids using tailored fuzzy features, taking into account the optimization of the available resources.

Many real-world classification systems must comply with a series of inherent restrictions to the problem at hand such as response times, power consumptions or computational costs. This poses a fundamental limitation to traditional performance-driven classifiers and learning algorithms by restraining their applicability in cost-sensitive scenarios. Because of this, fuzzy systems are leveraged to learn cost-conscious multi-stage classifiers through multiobjective optimization to find a set of optimal tradeoffs between accuracy and any related cost. This approach allows find a suitable balance between all objectives regardless of the scenario. Experimental evaluations were done for Sound Environment Classification in modern battery-powered hearing aids by jointly optimising classification accuracy and computational costs.

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.

The k-nearest neighbor method is a classifier based on the evaluation of the distances to each pattern in the training set. The edited version of this method consists of the application of this classifier with a subset of the complete training set in which some of the training patterns are excluded, in order to reduce the classification error rate. In recent works, genetic algorithms have been successfully applied to determine which patterns must be included in the edited subset. In this paper we propose a novel implementation of a genetic algorithm for designing edited k-nearest neighbor classifiers. It includes the definition of a novel mean square error based fitness function, a novel clustered crossover technique, and the proposal of a fast smart mutation scheme. In order to evaluate the performance of the proposed method, results using the breast cancer database, the diabetes database and the letter recognition database from the UCI machine learning benchmark repository have been included. Both error rate and computational cost have been considered in the analysis. Obtained results show the improvement achieved by the proposed editing method.

In this paper, the alignment of noisy high-resolution radar signals using the maximum position method is studied. The relationship between the shift estimation and the signal-to-noise ratio is considered. As a result, two analytical expressions are obtained that approximate the root-mean-square error of the difference in the shift estimation with and without noise. These two expressions allow us to improve the understanding of the sensitivity to noise of the Maximum Position alignment method.