Diego B. Haddad

Robust Acoustic Self-Localization of Mobile Devices

Self-localization of smart portable devices serves as foundation for several novel applications. This work proposes a set of algorithms that enable a mobile device to passively determine its position relative to a known reference with centimeter precision, based exclusively on the capture of acoustic signals emitted by controlled sources around it. The proposed techniques tackle typical practical issues such as reverberation, unknown speed of sound, line-of-sight obstruction, clock skew, and the need for asynchronous operation. After their theoretical developments and off-line simulations, the methods are assessed as real-time applications embedded into off-the-shelf mobile devices operating in real scenarios. When line of sight is available, position estimation errors are at most 4 cm using recorded signals.

Normalized Subband Adaptive Filtering Algorithm With Reduced Computational Complexity

Subband structures are suitable for improving convergence properties of adaptive filtering algorithms, particularly for colored input signals. This brief proposes a new subband adaptive algorithm with sparse adaptive subfilters, which employs the principle of minimal disturbance with multiple-constraint optimization. A performance analysis is carried out, resulting in an expression for the steady-state mean-square error. It is shown that the proposed algorithm, under some particular parameter choices, presents the same performance as that of the normalized subband adaptive filter, but with reduced computational complexity.

Transient analysis of l0-LMS and l0-NLMS algorithms

Sparsity-aware adaptive algorithms present some advantages over standard ones, specially due to the fact that they have faster convergence rate. This paper proposes a stochastic model for both l0-LMS and l0-NLMS algorithms, and carries out an accurate transient analysis of these algorithms without requiring the input signal to be white. Some previously unreported learning properties of these algorithms are revealed by the proposed framework, which are confirmed by experimental evidence. HighlightsAn accurate transient analysis of both l0-LMS and l0-NLMS algorithms is advanced.A stochastic model for predicting their MSD and MSE learning behaviors is proposed.The analysis employs weaker conditions, e.g., input signal whiteness is not required.Learning properties of these algorithms are revealed by the proposed framework.Steady-state results can be predicted as limiting cases of the transient analysis.

A New Initialization Method for Frequency-Domain Blind Source Separation Algorithms

Frequency-domain blind source separation (BSS) techniques have been proposed with the objective of increasing the speed and/or reducing the computational complexity of conventional algorithms, mainly for applications that involve large convolutive mixture filters. In this letter, we present a new initialization method for frequency-domain BSS algorithms employing estimates of the directions of arrival and time-frequency masking, that outperforms the classical pre-whitening initialization technique. Performance and convergence time results of the proposed approach are presented using a frequency-domain BSS algorithm that exploits higher-order frequency dependencies, employing real conference room recordings and simulated data.

New adaptive algorithms for identification of sparse impulse responses — Analysis and comparisons

The convergence of the classical adaptive filtering algorithms becomes slow when the number of coefficients is very large. However, in many applications, such as digital network and acoustical echo cancelers, the system being modeled presents sparse impulse response, that is, most of its coefficients have small magnitudes. In order to improve the convergence for these applications, several algorithms have been proposed recently, which employ individual step-sizes for the updating of the different coefficients. The adaptation step-sizes are made larger for the coefficients with larger magnitudes, resulting in a faster convergence for the most significant coefficients. In this paper, we give an overview of the most important adaptive algorithms developed for the fast identification of systems with sparse impulse responses. Their convergence rates are compared through computer simulations for the identification of the channel impulse responses in a digital network echo cancellation application. A theoretical analysis of an improved version of the PNLMS algorithm is presented.

A unified approach for sparsity-aware and maximum correntropy adaptive filters

Adaptive filters that employ sparse constraints or maximum correntropy criterion (MCC) have been derived from stochastic gradient techniques. This paper provides a deterministic optimization framework which unifies the derivation of such algorithms. The proposed framework has also the ability of providing geometric insights about the adaptive filter updating. New algorithms that exploit both impulse responses sparsity and MCC are proposed, and an estimate of their steady-state MSE is advanced. Simulations show the advantages of the proposed algorithms in the identification of a sparse system with non-Gaussian additive noise.

Affine Projection Subband Adaptive Filter With Low Computational Complexity

The affine projection algorithm (APA) and the normalized subband adaptive filter (NSAF) have been proposed to improve the convergence rate of the normalized least mean square algorithm for colored input signals. Recently, the improved multiband-structured subband adaptive filter (IMSAF) has been advanced, combining the APA and NSAF optimization approaches. The use of the IMSAF may result in accelerated convergence rate but at the expense of a large increase in computational complexity. In this brief, a reduced complexity subband adaptive algorithm is proposed, which employs sparse subfilters and a subband APA cost function. A variable-step-size method is developed, thereby providing fast convergence rate while ensuring small steady-state misadjustment.

Closed-form solutions for robust acoustic sensor localization

This paper deals with the localization of acoustic sensors based on signals emitted by loudspeakers at known positions. In particular, a model for distortions in time-of-flight (TOF) estimates applicable to the sensor localization problem is presented along with closed-form solutions with low computational cost. The proposed techniques are able to approximate the sensor position even when the TOFs are corrupted by an unknown delay, there is a sampling frequency mismatch between the A/D and D/A converters associated with sensor and loudspeakers, and the speed of sound is unknown. Simulations and an experiment on real data demonstrate that the proposed methods are able to estimate sensor positions with less than 2 cm of error in the evaluated scenarios.

Acoustic Sensor Self-Localization: Models and Recent Results

The wide availability of mobile devices with embedded microphones opens up opportunities for new applications based on acoustic sensor localization (ASL). Among them, this paper highlights mobile device self-localization relying exclusively on acoustic signals, but with previous knowledge of reference signals and source positions. The problem of finding the sensor position is stated as a function of estimated times-of-flight (TOFs) or time-differences-of-flight (TDOFs) from the sound sources to the target microphone, and the main practical issues involved in TOF estimation are discussed. Least-squares ASL solutions are introduced, followed by other strategies inspired by sound source localization solutions: steered-response power, which improves localization accuracy, and a new region-based search, which alleviates complexity. A set of complementary techniques for further improvement of TOF/TDOF estimates are reviewed: sliding windows, matching pursuit, and TOF selection. The paper proceeds with proposing a novel ASL method that combines most of the previous material, whose performance is assessed in a real-world example: in a typical lecture room, the method achieves accuracy better than 20 cm.

Evaluating the Influence of Mulsemedia Content in Reading

Augmented e-books provide the reader with a better quality of experience (QoE) by enriching the e-books with multimedia content and interactive elements. In the multimedia field, an improve in QoE has been performed by the use of sensory effects, creating the so-called mulsemedia applications. In this paper, it is considered that e-books augmented with mulsemedia content influence the reader perception of the history environment, besides improving its QoE. It is presented a mulsemedia setup with light, wind and audio effects, which are synchronized with a written history. Among other discoveries, the collected data suggest that such elements have a more significant influence on the reader when the environment description tend to be vague, whereas in scenes whose details are precisely described this influence is rather minored.