Manuel Utrilla-Manso

Multilayer perceptrons applied to traffic sign recognition tasks

The work presented in this paper suggests a Traffic Sign Recognition (TSR) system whose core is based on a Multilayer Perceptron (MLP). A pre-processing of the traffic sign image (blob) is applied before the core. This operation is made to reduce the redundancy contained in the blob, to reduce the computational cost of the core and to improve its performance. For comparison purposes, the performance of the a statistical method like the k-Nearest Neighbour (k-NN) is included. The number of hidden neurons of the MLP is studied to obtain the value that minimizes the total classification error rate. Once obtained the best network size, the results of the experiments with this parameter show that the MLP achieves a total error probability of 3.85%, which is almost the half of the best obtained with the k-NN.

A hearing aid simulator to test adaptive signal processing algorithms

This paper deals with the description of a hearing aid simulation tool. This tool simulates the real behavior of digital DSP-based hearing aids with the aim of getting a very promising performance, which can be used for further design and research, and for a better fitting of the hearing impaired patient. The main parameters to program are the noise reduction techniques and the compression and feedback reduction algorithms. Also any other configuration is possible due to the access to the simulated signals in the hearing aid. So we can get a very promising performance which can be used for further design and research and for a better fitting of the hearing impaired patient. Results using a multilevel multifrequency hearing aid with real data collected from 18 patients show how the multifrequency compression techniques adapt the normal perceptible sounds to the hearing impaired patient perceiving area.

An evolutionary algorithm to optimize the microphone array configuration for speech acquisition in vehicles

Speech acquisition using microphone arrays is included in a variety of trending applications. Multichannel speech enhancement based on spatial filtering aims at improving the quality of the acquired speech. The optimization of the filter coefficients has been the primary focus in beamformer design. However, the array configuration plays an important role in the quality of the speech acquisition system and it should also be optimized. In some applications, the possibilities for microphone placement are very large, and the search of the optimum solution, which involves exploring all possible microphone configurations, is an unfeasible task. This work presents a novel search algorithm based on evolutionary computation to approximate the optimum array configuration. A realistic car noise model based on real measurements is proposed and used in the design. The obtained results support the suitability of the method, notably improving the results obtained by linear arrays with the same number of elements, which are the typical arrays currently assembled in vehicles.

Tighter limit cycle bounds for digital filters

A procedure for obtaining tighter bounds on zero-input limit cycles is presented. The proposed new bounds are applicable to digital filters of arbitrary order, described in state-space formulation and implemented with fixed-point arithmetic. For the most part, we obtain smaller bounds than those reported in the literature, using a computationally efficient algorithm that is easy to implement and has a comparatively short execution time. Simulation results show the validity of the proposed theory.

Synchronization based on mixture alignment for sound source separation in wireless acoustic sensor networks

Desynchronization degrades the performance of many signal processing algorithms in Wireless Acoustic Sensor Networks. It is mainly caused by the different distances between the source and each node and by the clock phase offset and frequency skew. Classical solutions use clock synchronization protocols and algorithms in the communication layer, but these alternatives do not tackle the lack of synchronization caused by the distances between sources and nodes.In this paper, we present a novel study of the synchronization problem in acoustic sensor networks from a signal processing point of view. First, we propose a theoretical framework that allows us to study the effects of misalignment over any short-time based algorithm, focusing on the requirements of the effective length of the analysis time frame. From this framework, a theoretical synchronization delay is established aimed at reducing the required length of the time frame. Second, two novel alignment methods are developed and are tuned up to reduce the amount of synchronization information required for transmission. The results obtained demonstrate that our proposed methods represent a good solution in terms of performance over the quality of a standard Blind Source Separation algorithm, allowing us to reduce the transmission bandwidth required for synchronization data. HighlightsWe study the effects of misalignment over BSS algorithms in WASN.A theoretical synchronization relaxes the constraint over the time frame length.Two novel alignment methods are inspired in the theoretical synchronization.The proposed methods synchronize the mixtures with a reduced transmission bandwidth.

Divide and conquer approach to improve performance on ATR systems

In this paper different methods applied to the Automatic Target Recognition problem are studied. A database of High Range Resolution radar profiles of six kinds of aircrafts is used to study the performance of four classification methods: k-Nearest Neighbor method, Multilayer Perceptrons, Radial Basis Function Networks, and Support Vector Machines. Results obtained with these classifiers show a high correlation between two of the classes of targets that cause the majority of errors. We propose to split the task into two subtasks. A first one in which the classes of correlated targets are grouped in a single class, and a second one to distinguish between them. Different classifiers are studied to be applied to each subtask. Results demonstrate that Radial Basis Function Networks are very good classifiers for the main subtask, while Support Vector Machines are the best classification method, among the studied, to distinguish between the correlated targets.

A new mixing matrix estimation method based on the geometrical analysis of the sound separation problem

Nowadays, some of the most successful sound source separation methods are based on the assumption of sparse sources. A large number of those separation solutions consist of two parts: the mixing matrix estimation and the separation stages. Concerning the first part, many sparsity-based separation methods rely on the use of clustering techniques to identify the samples of the mixtures due to each sound source. With certain types of sources, such as speech, the assumption of sparsity is questionable and so, these stages do not perform correctly.In this paper, we present a new mixing matrix estimation procedure to overcome sparsity-based methods separating speech sources. Our novel proposal establishes a geometric relationship between the mixing parameters using some available information about the microphone array, such as, number and type of microphones or the distance between them. Using this relationship, the complex estimation of the level differences is avoided. Results demonstrate that our proposal outperforms mixing matrix estimation solutions in terms of both speech separation quality and speech intelligibility. HighlightsSound separation problems are geometrically studied in a novel way.A new mixing matrix estimation method is introduced.A theoretical relationship between time and level differences is determined.Only time differences must be estimated, avoiding the calculation of the level ones.Time differences are obtained with a classical algorithm robust to reverberation.

Distributed and collaborative sound environment information extraction in binaural hearing aids

Current research in the field of Wireless Acoustic Sensor Networks (WASN) is gradually introducing the use of sound spatial techniques in the field of binaural hearing aids, in which sound environment information must be extracted in order to tune up the main hearing aid algorithms. In binaural hearing aids, computational capability, memory and data transmission are strictly constrained, which makes the use of distributed and collaborative approaches suitable. This paper proposes solutions for the collaborative and distributed sound environment information extraction through the estimation of the different noise levels, analyzing both the performance and the computational and transmission requirements. Results demonstrate that the proposed distributed solutions highly reduce the transmission rate and the computational cost, while maintaining the accuracy in the estimations.

Noise model for car-embedded speech acquisition system design

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.

Distortion analysis in a new transmultiplexer for xDSL applications

A new approach to designing nearly perfect transmultiplexers based on cosine-modulated filter banks for xDSL applications is presented. We propose a new way of designing the prototype filter from which the transmitting and receiving filters are derived. The resulting TMUX systems do not have phase distortion, and amplitude distortion is almost null. The importance of the prototype filter design technique on the performance of the TMUX system is evaluated and a comparative analysis with other very efficient methods is realized.