This paper introduces a physics-informed adaptive filtering framework for acoustic echo cancellation (AEC). Unlike conventional adaptive algorithms that rely solely on data-driven error minimization, the proposed method incorporates physically motivated priors derived from acoustic wave propagation and room impulse response structure. The echo path estimation problem is formulated as a composite stochastic optimization task, where the instantaneous squared error is regularized by constraints encoding causality, exponential energy decay, time-weighted sparsity of early reflections, spectral smoothness, and slow temporal variation of the acoustic path. The resulting Physics-Informed Normalized Least-Mean-Squares (PI-NLMS) algorithm performs stochastic gradient descent on the regularized cost while enforcing hard causality through projection. The proposed formulation restricts adaptation to a physically plausible echo-path manifold, improving conditioning and reducing variance without substantially increasing computational complexity. Theoretical analysis establishes mean convergence conditions and characterizes the bias-variance trade-off introduced by structured regularization. Simulation results under stationary and time-varying echo paths demonstrate faster convergence, improved steady-state misalignment, and enhanced echo return loss enhancement (ERLE) compared to conventional NLMS and sparsity-aware baselines.

Post-earthquake damage assessment is a critical step in disaster response and recovery. This paper introduces an innovative approach leveraging the EfficientNet-B0 neural network combined with wavelet-based data fusion (early and intermediate fusion strategies) to classify earthquake-related damage to existing buildings. The classification framework distinguishes between three damage levels: slight, moderate, and severe, in line with the standards defined by the Italian AeDES manual. The core innovation lies in the creation of a robust and expertly validated database, derived from a restructured and enhanced version of the PHI-Net dataset and a newly curated dataset based on the Macroseismic Photographic Database (DFM) from the Italian National Institute of Geophysics and Volcanology (INGV). This last dataset incorporates samples from various documented Italian earthquakes. The proposed method aims at supporting field operators by automating the damage classification step, thus streamlining the compilation of AeDES forms. Results demonstrate that the proposed method achieves a higher accuracy and reliability, compared to other state-of-the-art strategies, offering a significant contribution to automated seismic damage assessment.

Environmental Sound Classification (ESC) is becoming an ever increasingly important application in different scenarios, such as smart cities, autonomous systems, safety, and industrial monitoring. Traditional methods for ESC mainly rely on features extracted from a single-representation, usually spectrograms or MFCCs. However, while deep learning-based CNN models have demonstrated excellent performance, they still suffer from certain limitations due to the reliance on a single feature representation. In this regard, this work exploits a multi-representation strategy by fusing five kinds of audio features, namely: spectrograms, phasograms, scalograms, wavelet phasograms, and MFCC-grams. Each representation captures different properties of the audio. These representations are combined in a structured manner by investigating three fusion strategies: early, intermediate, and late fusion using a novel model based on the EfficientNet, named EfficientAudioNet. The proposed strategies are evaluated on four benchmark datasets: a Construction Site machinery sounds dataset, the ESC-10 and ESC-50 environmental sound datasets, and the UrbanSound8K dataset. Experimental results demonstrate that the multi-representation fusion, specially the early fusion, significantly enhances the classification performance. Overall, the proposed approach overcomes state-of-the-art accuracy on all the tested datasets.

The detection of the COronaVIrus Disease 2019 (COVID-19) from Computed Tomography (CT) scans has become a very important task in modern medical diagnosis. Unfortunately, typical resolutions of state-of-the-art CT scans are still not adequate for reliable and accurate automatic detection of COVID-19 disease. Motivated by this consideration, in this paper, we propose a novel architecture that jointly affords the Single-Image Super-Resolution (SISR) and the reliable classification problems from Low Resolution (LR) and noisy CT scans. Specifically, the proposed architecture is based on a couple of Twinned Residual Auto-Encoders (TRAE), which exploits the feature vectors and the SR images recovered by a Master AE for performing transfer learning and then improves the training of a “twinned” Follower AE. In addition, we also develop a Task-Aware (TA) version of the basic TRAE architecture, namely the TA-TRAE, which further utilizes the set of feature vectors generated by the Follower AE for the joint training of an additional auxiliary classifier, so to perform automated medical diagnosis on the basis of the available LR input images without human support. Experimental results and comparisons with a number of state-of-the-art CNN/GAN/CycleGAN benchmark SISR architectures, performed by considering ×2, ×4, and ×8 super-resolution (i.e., upscaling) factors, support the effectiveness of the proposed TRAE/TA-TRAE architectures. In particular, the detection accuracy attained by the proposed architectures outperforms the corresponding ones of the implemented CNN, GAN and CycleGAN baselines up to 9.0%, 6.5%, and 6.0% at upscaling factors as high as ×8.

Abstract Bidirectional generative adversarial networks (BiGANs) and cycle generative adversarial networks (CycleGANs) are two emerging machine learning models that, up to now, have been used as generative models, i.e., to generate output data sampled from a target probability distribution. However, these models are also equipped with encoding modules, which, after weakly supervised training, could be, in principle, exploited for the extraction of hidden features from the input data. At the present time, how these extracted features could be effectively exploited for classification tasks is still an unexplored field. Hence, motivated by this consideration, in this paper, we develop and numerically test the performance of a novel inference engine that relies on the exploitation of BiGAN and CycleGAN-learned hidden features for the detection of COVID-19 disease from other lung diseases in computer tomography (CT) scans. In this respect, the main contributions of the paper are twofold. First, we develop a kernel density estimation (KDE)-based inference method, which, in the training phase, leverages the hidden features extracted by BiGANs and CycleGANs for estimating the (a priori unknown) probability density function (PDF) of the CT scans of COVID-19 patients and, then, in the inference phase, uses it as a target COVID-PDF for the detection of COVID diseases. As a second major contribution, we numerically evaluate and compare the classification accuracies of the implemented BiGAN and CycleGAN models against the ones of some state-of-the-art methods, which rely on the unsupervised training of convolutional autoencoders (CAEs) for attaining feature extraction. The performance comparisons are carried out by considering a spectrum of different training loss functions and distance metrics. The obtained classification accuracies of the proposed CycleGAN-based (resp., BiGAN-based) models outperform the corresponding ones of the considered benchmark CAE-based models of about 16% (resp., 14%).

Nonlinear models are known to provide excellent performance in real-world applications that often operate in nonideal conditions. However, such applications often require online processing to be performed with limited computational resources. To address this problem, we propose a new class of efficient nonlinear models for online applications. The proposed algorithms are based on linear-in-the-parameters (LIPs) nonlinear filters using functional link expansions. In order to make this class of functional link adaptive filters (FLAFs) efficient, we propose low-complexity expansions and frequency-domain adaptation of the parameters. Among this family of algorithms, we also define the partitioned-block frequency-domain FLAF (FD-FLAF), whose implementation is particularly suitable for online nonlinear modeling problems. We assess and compare FD-FLAFs with different expansions providing the best possible tradeoff between performance and computational complexity. Experimental results prove that the proposed algorithms can be considered as an efficient and effective solution for online applications, such as the acoustic echo cancellation, even in the presence of adverse nonlinear conditions and with limited availability of computational resources.

Generative audio data augmentation in a construction site is one of challenging research areas due to the high dissimilarity between work sounds of involved machines and equipment. However, it becomes necessary since the availability of audio data of critical work classes is often rare. Motivated by these considerations and demands, in this paper, we propose a complex-valued GAN architecture working with the audio spectrogram, named CoVal-SGAN, for an effective augmentation of audio data. Specifically, the proposed CoVal-SGAN exploits both the magnitude and phase information to improve the quality of the artificially generated audio signals and increase the overall performance of the underlying classifier. Numerical results, performed on the data recorded in real-world construction sites, along with the comparisons with available state-of-the-art approaches, show the effectiveness of the proposed idea by obtaining an improved accuracy.

Abstract We present a probabilistic method for classifying chest computed tomography (CT) scans into COVID-19 and non-COVID-19. To this end, we design and train, in an unsupervised manner, a deep convolutional autoencoder (DCAE) on a selected training data set, which is composed only of COVID-19 CT scans. Once the model is trained, the encoder can generate the compact hidden representation (the hidden feature vectors) of the training data set. Afterwards, we exploit the obtained hidden representation to build up the target probability density function (PDF) of the training data set by means of kernel density estimation (KDE). Subsequently, in the test phase, we feed a test CT into the trained encoder to produce the corresponding hidden feature vector, and then, we utilise the target PDF to compute the corresponding PDF value of the test image. Finally, this obtained value is compared to a threshold to assign the COVID-19 label or non-COVID-19 to the test image. We numerically check our approach’s performance (i.e. test accuracy and training times) by comparing it with those of some state-of-the-art methods.

In this paper, we consider the design of deep neural networks augmented with multiple auxiliary classifiers departing from the main (backbone) network. These classifiers can be used to perform early-exit from the network at various layers, making them convenient for energy-constrained applications such as IoT, embedded devices, or Fog computing. However, designing an optimized early-exit strategy is a difficult task, generally requiring a large amount of manual fine-tuning. In this paper, we propose a way to jointly optimize this strategy together with the branches, providing an end-to-end trainable algorithm for this emerging class of neural networks. We achieve this by replacing the original output of the branches with a 'soft', differentiable approximation. In addition, we also propose a regularization approach to trade-off the computational efficiency of the early-exit strategy with respect to the overall classification accuracy. We evaluate our proposed design approach on a set of image classification benchmarks, showing significant gains in accuracy and inference time.

Frequency-domain adaptive filters (FDAFs) have been widely used over the years, but they are still matter of research due to their powerful capabilities that differentiate them from the whole family of time-domain adaptive filters. This paper aims at providing an overview on FDAFs through a unifying framework that can be used for the derivation of the most popular algorithms of the FDAF family and enables the processing of a wide variety of signals, from real-valued ones to complex- and hypercomplex-valued signals. In particular, we focus on a recent class of FDAFs in the quaternion domain and we show how to derive it from the described framework. Moreover, we evaluate the application of the derived quaternion FDAF to the processing of 3D audio signals. Experimental results show the effectiveness of the proposed adaptive filter in estimating the inverse of a multidimensional acoustic impulse response.

Linear-in-the-parameters nonlinear adaptive filters often show some sparse behavior due to the fact that not all the coefficients are equally useful for the modeling of any nonlinearity. Recently, proportionate algorithms have been proposed to leverage sparsity behaviors in nonlinear filtering. In this paper, we deal with this problem by introducing a proportionate adaptive algorithm based on an ℓ1-norm penalty of the cost function, which regularizes the solution, to be used for a class of nonlinear filters based on functional links. The proposed algorithm stresses the difference between useful and useless functional links for the purpose of nonlinear modeling. Experimental results clearly show faster convergence performance with respect to the standard (i.e., non-regularized) version of the algorithm.

In this paper, we focus on the problem of removing noise in the acoustic domain. To this end, we introduce a class of hybrid nonlinear spline filters, which are designed as a cascade of an adaptive spline function and a single layer adaptive nonlinear network. The adaptive nonlinear networks employed in this work are the functional link network and the even mirror Fourier nonlinear network. Suitable update rules, which not only update the adaptive weights of the nonlinear networks, but also introduce adaptability in the developed spline function are derived. The proposed nonlinear filters have been successfully applied to nonlinear system identification as well as nonlinear active noise control. The new filters have been shown to outperform other popular nonlinear filters.

Nonlinear distortions pose a serious problem for the quality preservation of audio and speech signals. To address this problem, such signals are processed by nonlinear models. Functional link adaptive filter (FLAF) is a linear-in-the-parameter nonlinear model, whose nonlinear transformation of the input is characterized by a basis function expansion, satisfying the universal approximation properties. Since the expansion type affects the nonlinear modeling according to the nature of the input signal, in this paper we investigate the FLAF modeling performance involving the most popular functional expansions when audio and speech signals are processed. A comprehensive analysis is conducted to provide the best suitable solution for the processing of nonlinear signals. Experimental results are assessed also in terms of signal quality and intelligibility.

Acoustic echo cancellers are used in teleconferencing systems in order to reduce undesired echoes due to coupling between microphones and loudspeakers. Stereophonic systems provide more realistic experience than single-channel systems, since listeners have spatial information that helps to identify the speaker position. Assuming this scenario, a suitable choice for the system parameters becomes essential to improve the audio reproduction quality. Error-driven optimization strategies are usually used to obtain an optimal system configuration but there is no relationship with the quality desired by the user. In this paper, an interactive evolutionary algorithm is adopted for a stereophonic acoustic echo cancellation system in order to meet subjective specifications in the optimization stage. In this way, the optimal system configuration is derived according to a user-driven approach in order to satisfy the quality requirements demanded by users availing such stereophonic systems. Experimental results prove the effectiveness of the proposed interactive architecture according to both objective and subjective measures.

Immersive speech communication systems have been gaining increasing attention due to their ability to reproduce enhanced acoustic images, and thus achieving good performance in terms of sound quality and accuracy. In this context , a fundamental role is played by intelligent acoustic interfaces (IAIs), which aim at acquiring and/or reproducing desired acoustic information with enhanced perception. The recent widespread availability of multimedia devices, equipped with different kind of sensors, has broadened the range of data processing methods, thus giving a chance for developing advanced IAIs. In this paper, we propose an immersive communication system composed of two IAIs: the first one exploits microphones and cameras, together with a signal processing system, to reduce unwanted noise and enhance the speech quality of the desired information in the transmitting room; the second one is an advanced reproduction system based on a loudspeaker array and on an effective wave field synthesis technique capable of reproducing the spatial perception of the desired speech source in the receiving room. The whole system has been assessed in simulated and real immersive communication scenarios: objective and subjective evaluations have been shown the effectiveness of the proposed system.

The extreme learning machine (ELM) was recently proposed as a unifying framework for different families of learning algorithms. The classical ELM model consists of a linear combination of a fixed number of nonlinear expansions of the input vector. Learning in ELM is hence equivalent to finding the optimal weights that minimize the error on a dataset. The update works in batch mode, either with explicit feature mappings or with implicit mappings defined by kernels. Although an online version has been proposed for the former, no work has been done up to this point for the latter, and whether an efficient learning algorithm for online kernel-based ELM exists remains an open problem. By explicating some connections between nonlinear adaptive filtering and ELM theory, in this brief, we present an algorithm for this task. In particular, we propose a straightforward extension of the well-known kernel recursive least-squares, belonging to the kernel adaptive filtering (KAF) family, to the ELM framework. We call the resulting algorithm the kernel online sequential ELM (KOS-ELM). Moreover, we consider two different criteria used in the KAF field to obtain sparse filters and extend them to our context. We show that KOS-ELM, with their integration, can result in a highly efficient algorithm, both in terms of obtained generalization error and training time. Empirical evaluations demonstrate interesting results on some benchmarking datasets.

In kernel-based learning methods, a crucial design parameter is given by the choice of the kernel function to be used. Although there is, in theory, an infinite range of potential candidates, a handful of kernels covers the majority of actual applications. Partly, this is due to the difficulty of choosing an optimal kernel function in absence of a-priori information. In this respect, Genetic Programming (GP) techniques have shown interesting capabilities of learning non-trivial kernel functions that outperform commonly used ones. However, experiments have been restricted to the use of Support Vector Machines (SVMs), and have not addressed some problems that are specific to GP implementations, such as diversity maintenance. In these respects, the aim of this paper is twofold. First, we present a customized GP-based kernel search method that we apply using an L2-Regularization Network as the base learning algorithm. Second, we investigate the problem of diversity maintenance in the context of kernel evolution, and test an adaptive criterion for maintaining it in our algorithm. For the former point, experiments show a gain in accuracy for our method against fine-tuned standard kernels. For the latter, we show that diversity is decreasing critically fast during the GP iterations, but this decrease does not seems to affect performance of the algorithm.

Nowadays, there is a large interest towards multimedia audio systems as a consequence of the development of advanced digital signal processing techniques. In particular, immersive speech communication system has gaining increasing attention since they allow to reproduce realistic acoustic image, and thus achieving good performance in terms of sound quality and accuracy. In this scenario a fundamental role is played by intelligent acoustic interfaces which aim at acquiring audio information, processing it, and returning the processed information to the audio rendering system. In this paper, an effective intelligent acoustic interface composed of a microphone array and a signal processing system capable to enhance the intelligibility of the desired information of the transmitting room is proposed, combined with an advanced reproduction system based on an efficient application of a wave field synthesis technique capable to reproduce an immersive scenario in the receiving room. The whole system has been assessed within a speech communication application involving a moving desired source in a real scenario: objective and subjective evaluation has been reported in order to show the overall system performance.

Echo State Networks (ESNs) were introduced to simplify the design and training of Recurrent Neural Networks (RNNs), by explicitly subdividing the recurrent part of the network, the reservoir, from the non-recurrent part. A standard practice in this context is the random initialization of the reservoir, subject to few loose constraints. Although this results in a simple-to-solve optimization problem, it is in general suboptimal, and several additional criteria have been devised to improve its design. In this paper we provide an effective algorithm for removing redundant connections inside the reservoir during training. The algorithm is based on the correlation of the states of the nodes, hence it depends only on the input signal, it is efficient to implement, and it is also local. By applying it, we can obtain an optimally sparse reservoir in a robust way. We present the performance of our algorithm on two synthetic datasets, which show its effectiveness in terms of better generalization and lower computational complexity of the resulting ESN. This behavior is also investigated for increasing levels of memory and non-linearity required by the task.

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.

Binaural sound source localization can be performed by imitation of the fundamental mechanisms of the human auditory system, which is based on the integrated effects of ear, pinnae, head and torso. In particular, two physical cues can be exploited, i.e. the Interaural Time Difference (ITD) and the Interaural Level Difference (ILD). It is known that joint use of ITD and ILD provides good source azimuth estimations. In many practical situations binaural localization has to be performed in closed environments, where the presence of reverberation degrades the performance of available position estimators. In this paper a possible solution to this difficult problem is introduced. The proposed solution is based on proper use of cepstral prefiltering prior to source localization by ITD and ILD. It is shown that cepstrum can help in reducing the effects of reverberation, thus yielding better location estimates.

This paper describes a new adaptive algorithm and assesses its effectiveness within speech enhancement applications. The proposed variable step size block exact APA (VSS-BEAPA) filtering is based on the affine projection algorithm (APA) and introduces a block processing with a variable step size that allows to consider under-modeling scenarios. The algorithm shows improved convergence performance and computational efficiency and its robustness is proved in a typical context of a hands-free teleconferencing application in a noisy environment. The experiments show that a microphone array system joined with VSS-BEAPA filtering is capable of both decreasing the noise level and enhancing the speech signal quality.

This paper introduces an Independent Component Analysis (ICA) approach to the separation of nonlinear mixtures in the complex domain. Source separation is performed by a complex INFOMAX approach. The neural network which realizes the separation employs the so called "Mirror Model" and is based on adaptive activation functions, whose shape is properly modified during learning. Nonlinear functions involved in the processing of complex signals are realized by pairs of spline neurons called "splitting functions", working on the real and the imaginary part of the signal respectively. Theoretical proof of existence and uniqueness of the solution under proper assumptions is also provided. In particular a simple adaptation algorithm is derived and some experimental results that demonstrate the effectiveness of the proposed solution are shown.