The ’Footsteps Illusion’ shows that pitch-altering footstep sounds in real-time affects body perception, gait, and emotion: high versus low frequencies evoke a lighter versus heavier body. We tested whether this illusion extends beyond the lab, where environmental factors matter. Using a mixed-methods approach, twenty-eight participants used a minimal setup to synchronize with three prerecorded pitch-altered footstep soundtracks: Control, High-Frequency, and Low-Frequency. In Experiment 1, they walked a fixed path, with gait recordings, questionnaires, and body visualizations. In Experiment 2, they walked freely across campus routes with High/Low-Frequency soundtracks, followed by post-walk interviews using a novel spatial mapping tool linking body sensations to context. Results replicate the illusion outdoors, showing synchronization with prerecorded sounds as a viable alternative to real-time feedback, and revealing that contextual factors modulate illusion effects. We contribute insights and novel tools (prototype, spatial mapping method, and footstep soundbase) for studying multisensory body perception in everyday contexts.

Changes in body perception influence behavior and emotion and can be induced through multisensory feedback. Auditory feedback to one’s actions can trigger such alterations; however, it is unclear which individual factors modulate these effects. We employ and evaluate SoniWeight Shoes, a wearable device based on literature for altering one’s weight perception through manipulated footstep sounds. In a healthy population sample across a spectrum of individuals (n=84) with varying degrees of eating disorder symptomatology, physical activity levels, body concerns, and mental imagery capacities, we explore the effects of three sound conditions (low-frequency, high-frequency and control) on extensive body perception measures (demographic, behavioral, physiological, psychological, and subjective). Analyses revealed an impact of individual differences in each of these dimensions. Besides replicating previous findings, we reveal and highlight the role of individual differences in body perception, offering avenues for personalized sonification strategies. Datasets, technical refinements, and novel body map quantification tools are provided.

Diffusion kernel algorithms are interesting tools for distributed nonlinear estimation. However, for the sake of feasibility, it is essential in practice to restrict their computational cost and the number of communications. In this paper, we propose a censoring algorithm for adaptive kernel diffusion networks based on random Fourier features that locally adapts the number of nodes censored according to the estimation error. It presents fast convergence during the transient phase and a significant reduction in the number of censored nodes in the steady state, thus reducing the energy consumption and the computational cost mainly by decreasing the amount of communication between nodes. Simulation results show that the proposed technique can significantly decrease the computational cost with less impact on the convergence rate when compared to existing solutions.

A model-based pre-equalizer for propagation-induced distortion of acoustic waveforms in an air-filled waveguide at high sound pressure levels is developed. The nonlinear distortions introduced by propagation are modeled using Burgers propagation model. This equalizer is stimulus-independent and mitigates nonlinear distortion at some predefined distance in the waveguide. The pre-equalizer digitally propagates the waveform backwards using a sign-inverted propagation model. The output of the pre-equalizer is fed to the waveguide. When the forward model completely characterizes the waveguide, the waveforms arriving at the chosen destination will be identical to a delayed version of the input signal fed to the pre-equalizer. Experimental evaluations employing sinusoidal and multitone stimuli demonstrated approximately a two-fold reduction in the intermodulation distortion and a four-fold reduction in total harmonic distortion at low frequencies. These results represented a two-fold improvement over a model-based method available in the literature.

In this paper, we propose a scheme to simplify the selection of kernel adaptive filters in a multikernel structure. By multiplying the output of each kernel filter by an adaptive biasing factor between zero and one, the degrading effects of poorly adjusted kernel filters can be minimized, increasing the robustness of the multikernel scheme. This approach is able to deal with the lack of the necessary statistical information for an optimal adjustment of the filter and its structure. The advantages of the proposed scheme with respect to other multi-kernel solutions are checked by means of numerical examples in the context of signal prediction and system identification.

Room equalization has become essential for sound reproduction systems to provide the listener with the desired acoustical sensation. Recently, adaptive filters have been proposed as an effective tool in the core of these systems. In this context, this paper introduces different novel schemes based on the combination of adaptive filters idea: a versatile and flexible approach that permits obtaining adaptive schemes combining the capabilities of several independent adaptive filters. In this way, we have investigated the advantages of a scheme called combination of block-based adaptive filters which allows a blockwise combination splitting the adaptive filters into nonoverlapping blocks. This idea was previously applied to the plant identification problem, but has to be properly modified to obtain a suitable behavior in the equalization application. Moreover, we propose a scheme with the aim of further improving the equalization performance using the a priori knowledge of the energy distribution of the optimal inverse filter, where the block filters are chosen to fit with the coefficients energy distribution. Furthermore, the biased block-based filter is also introduced as a particular case of the combination scheme, especially suited for low signal-to-noise ratios (SNRs) or sparse scenarios. Although the combined schemes can be employed with any kind of adaptive filter, we employ the filtered-x improved proportionate normalized least mean square algorithm as basis of the proposed algorithms, allowing to introduce a novel combination scheme based on partitioned block schemes where different blocks of the adaptive filter use different parameter settings. Several experiments are included to evaluate the proposed algorithms in terms of convergence speed and steady-state behavior for different degrees of sparseness and SNRs.

Traditional acoustic echo cancelers use a linear model to represent the echo path. Nevertheless, many consumer devices include loudspeakers and audio power amplifiers that may generate significant nonlinear distortions, creating the need for acoustic echo cancelers to produce a nonlinear filter response. To address this issue, we propose a nonlinear acoustic echo cancellation algorithm based on the framework of kernel methods. We model the echo path as a Hammerstein system, and we propose a resource-efficient strategy to identify the nonlinear and linear parts. While the basic algorithm is presented as an iterative batch method, we show that a simple extension allows it to be used in online scenarios as well. Results for both types of scenarios show that the algorithm produces good results on a system with a clipping nonlinearity and a realistic room impulse response.

Recently, a new class of nonlinear adaptive filtering architectures has been introduced based on the functional link adaptive filter (FLAF) model. Here we focus specifically on the split FLAF (SFLAF) architecture, which separates the adaptation of linear and nonlinear coefficients using two different adaptive filters in parallel. This property makes the SFLAF a well-suited method for problems like nonlinear acoustic echo cancellation (NAEC), in which the separation of filtering tasks brings some performance improvement. Although flexibility is one of the main features of the SFLAF, some problem may occur when the nonlinearity degree of the input signal is not known a priori. This implies a non-optimal choice of the number of coefficients to be adapted in the nonlinear path of the SFLAF. In order to tackle this problem, we propose a proportionate FLAF (PFLAF), which is based on sparse representations of functional links, thus giving less importance to those coefficients that do not actively contribute to the nonlinear modeling. Experimental results show that the proposed PFLAF achieves performance improvement with respect to the SFLAF in several nonlinear scenarios.

This paper introduces a new class of nonlinear adaptive filters, whose structure is based on Hammerstein model. Such filters derive from the functional link adaptive filter (FLAF) model, defined by a nonlinear input expansion, which enhances the representation of the input signal through a projection in a higher dimensional space, and a subsequent adaptive filtering. In particular, two robust FLAF-based architectures are proposed and designed ad hoc to tackle nonlinearities in acoustic echo cancellation (AEC). The simplest architecture is the split FLAF, which separates the adaptation of linear and nonlinear elements using two different adaptive filters in parallel. In this way, the architecture can accomplish distinctly at best the linear and the nonlinear modeling. Moreover, in order to give robustness against different degrees of nonlinearity, a collaborative FLAF is proposed based on the adaptive combination of filters. Such architecture allows to achieve the best performance regardless of the nonlinearity degree in the echo path. Experimental results show the effectiveness of the proposed FLAF-based architectures in nonlinear AEC scenarios, thus resulting an important solution to the modeling of nonlinear acoustic channels.

The combination of filters concept is a simple and flexible method to circumvent various compromises hampering the operation of adaptive linear filters. Recently, applications which require the identification of not only linear, but also nonlinear systems are widely studied. In this paper, we propose a combination of adaptive Volterra filters as the most versatile nonlinear models with memory. Moreover, we develop a novel approach that shows a similar behavior but significantly reduces the computational load by combining Volterra kernels rather than complete Volterra filters. Following an outline of the basic principles, the second part of the paper focuses on the application to nonlinear acoustic echo cancellation scenarios. As the ratio of the linear to nonlinear echo signal power is, in general, a priori unknown and time-variant, the performance of nonlinear echo cancellers may be inferior to a linear echo canceller if the nonlinear distortion is very low. Therefore, a modified version of the combination of kernels is developed obtaining a robust behavior regardless of the level of nonlinear distortion. Experiments with noise and speech signals demonstrate the desired behavior and the robustness of both the combination of Volterra filters and the combination of kernels approaches in different application scenarios.

Combinations of adaptive filters have attracted attention as a simple solution to improve filter performance, including tracking properties. In this paper, we consider combinations of LMS and RLS filters, and study their performance for tracking time-varying solutions. We show that a combination of two filters from the same family (i.e., two LMS or two RLS filters) cannot improve the performance over that of a single filter of the same type with optimal selection of the step size (or forgetting factor). However, combining LMS and RLS filters it is possible to simultaneously outperform the optimum LMS and RLS filters. In other words, combination schemes can achieve smaller errors than optimally adjusted individual filters. Experimental work in a plant identification setup corroborates the validity of our results.

This paper presents a method for estimating the optimum number of second-order kernel diagonals of an adaptive Volterra filter in system identification tasks. To this end, a recently proposed time-domain mechanism is carried over to the very efficient partitioned-block DFT-domain Volterra filtering technique. The size of the nonlinear memory is controlled by monitoring the performance of an adaptive combination scheme with two differently-sized quadratic kernels. Subsequently, an efficient version is derived, requiring only minor additional computations as compared to a single Volterra filter. The effectiveness of the outlined estimation procedure is demonstrated by various simulations with real nonlinear systems and both noise and speech inputs in an acoustic echo cancellation scenario.

Adaptive combinations of adaptive filters are gaining popularity as a flexible and versatile solution to improve adaptive filters performance. In the recent years, combination schemes have focused on two different approaches: Convex and affine combinations, developing principally practical implementations with just two component filters. However, combinations of a higher number of adaptive filters can offer additional advantages, mainly in tracking environments. In this paper, we introduce a practical adaptation scheme for the affine combination of an arbitrary number of filters, including a steady-state analysis where the proposed rule is compared with the optimal combination. Several experiments both in tracking and stationary scenarios serve to demonstrate the appropriate performance of this approach.

Nonlinear acoustic echo cancellers (NLAEC) are becoming increasingly important in hands-free applications. However, in some situations, an NLAEC is inferior to a linear AEC, especially when the channel generates a negligible (or no) nonlinear echo. In general, the ratio of the linear to nonlinear echo signal power is unknown a priori, and will vary over time, thus making it difficult to know if an NLAEC would improve or degrade the cancellation. In this paper, we present two novel solutions to this problem based on the adaptive combination of linear and nonlinear echo cancellers. Both solutions perform efficiently regardless of the level of nonlinear echo. The benefits and robustness of both schemes are illustrated by experiments using Laplacian colored noise and speech input signals.

This paper presents a method for estimating the optimum memory size for identification of an unknown second-order Volterra kernel. As these structures may imply considerable computational demands, it is highly desirable to design adaptive realizations with a minimum number of coefficients. Therefore, we propose a combination scheme comprising two Volterra filters with time-variant sizes of the actually used quadratic kernels. By following some simple rules, the number of diagonals in the quadratic kernels is increased or decreased in order to find the optimum memory configuration in parallel to the coefficient adaptation. Thus, the arbitrary choice of the nonlinear system size is overcome by a dynamically growing/shrinking system. Experimental results for various signals and nonlinear scenarios demonstrate the effectiveness of the proposed method.

Adaptive filtering schemes are subject to different tradeoffs regarding their steady-state misadjustment, speed of convergence, and tracking performance. To alleviate these compromises, a new approach has recently been proposed, in which two filters with complementary capabilities adaptively mix their outputs to get an overall filter of improved performance. Following this approach, we propose a new normalized rule for adapting the mixing parameter that controls the combination. The new update rule preserves the good features of the existing scheme and is more robust to changes in the filtering scenario, for instance when the signal-to-noise ratio (SNR) is time varying. The benefits of the normalized scheme are illustrated analytically and with a number of experiments in both stationary and tracking situations.