This study compares data-driven and non-data-driven methods for classifying dysarthric speech employing the UA-Speech dataset. Data-driven deep-learning architectures, including MobileNetV3, ResNet50, and ResNet152, are evaluated alongside non-data-driven approaches such as pre-trained Automatic Speech Recognition (ASR) based methods and DSP-based methods. The findings reveal that overlapping patient data between training and testing sets leads to inflated performance metrics. By enforcing patient-separated evaluations, the study highlights reduced model accuracy, emphasizing the need for methodologies that generalize effectively to unseen data. Among the tested approaches, the ASR-based approach demonstrates the highest potential, achieving strong correlation with human intelligibility assessments and offering promising clinical applicability. This analysis underscores the importance of robust evaluation protocols and highlights the potential of pre-trained ASR models for reliable dysarthric speech assessment.

The creation of Personal Sound Zones (PSZ) is a recent application of digital signal processing that allows differentiating the sound intensity in neighboring regions of space (e.g. a “bright” and a “dark” zone). Given the impulse responses of the environment, digital filters can be designed in order to obtain an attenuation of the signal in the dark zone as a result of the superposition of the filtered IR coming from each loudspeaker. A neural optimization approach was recently shown to enable PSZ by designing digital FIR filters. In this work we propose an improvement of that neural optimization approach using a simpler neural network architecture. Furthermore we extend the method to the design of IIR filters, which is computationally more effective for a real-time implementation. The neural technique is compared with two state-of-the-art methods, analyzing the performance in terms of Acoustic Contrast. Experiments have been performed using a vehicle composed of standard loudspeakers and two speaker arrays, and show that the proposed approach achieves remarkable Acoustic Contrast without sacrificing audio quality.

In the last decades many efficient methods have been proposed to generate waveforms with reduced aliasing, proving this field quite mature. However, the introduction of Antiderivative Antialiasing (AA) methods for the reduction of aliasing in nonlinear discrete-time processing, can shed a new light on bandlimited oscillators and provide a general method for dealing with aliasing in arbitrary waveforms generation. In this work we will first bridge the gap between AA methods and bandlimited waveform generation, and then show the flexibility of the recently introduced AA-IIR method for dealing with both classical synthesizer waveforms and arbitrary wavetables. We will show how AA methods can be used for periodic and non-periodic waveform generation, and provide an innovative AA-IIR method able to compute alias-reduced version of any waveform with arbitrary antialiasing filter order, without the effort of computing ad hoc analytical expressions. Antialiasing performance is compared with other well known methods, showing the effectiveness of the approach.

This paper describes a novel Deep Learning method for the design of IIR parametric filters for automatic multipoint audio equalization, that is the task of improving the sound quality of a listening environment at multiple listening points employing multiple loudspeakers. The filters are designed to approximate the inverse of the RIR and achieve almost flat magnitude response. A simple and effective neural architecture, named BiasNet, is proposed to determine the IIR equalizer parameters. This novel architecture is conceived for optimization and, as such, is able to produce optimal IIR equalizer parameters at its output, after training, with no input required. In absence of input, the presence of learnable non-zero bias terms ensures that the network works properly. An output scaling method is used to obtain accurate tuning of the IIR filters center frequency, quality factor and gain. All layers involved in the proposed method are shown to be differentiable, allowing backpropagation to optimize the network weights and achieve, after a number of training iterations, the optimal output according to a given RIR. The parameters are optimized with respect to a loss function based on a spectral distance between the measured and desired magnitude response, and a regularization term is used to keep the same microphone-loudspeaker energy balance after equalization. Two experimental scenarios are employed, a room and a car cabin, with several loudspeakers. The performance of the proposed method improves over the baseline techniques and achieves an almost flat band at a lower computational cost.

Nonlinear digital circuits and waveshaping are active areas of study, specifically for what concerns numerical and aliasing issues. In the past, an effective method was proposed to discretize nonlinear static functions with reduced aliasing based on the antiderivative of the nonlinear function. Such a method is based on the continuous-time convolution with an FIR antialiasing filter kernel, such as a rectangular kernel. These kernels, however, are far from optimal for the reduction of aliasing. In this paper we introduce the use of arbitrary IIR rational transfer functions that allow a closer approximation of the ideal antialiasing filter, required in the fictitious continuous-time domain before sampling the nonlinear function output. These allow a higher degree of aliasing reduction and can be flexibly adjusted to balance performance and computational cost.

In this paper, we address the problem of the concurrent detection of multiple infant cries by using microphones located in the cribs of a Neonatal Intensive Care Unit (NICU). We term this task as infant cry diarization in resemblance with the "speaker diarization" task related to the speech signal: instead of determining "who spoke when", here the problem is determining "who cried when". The proposed algorithm consists of a fully-convolutional neural network (Conv-DetNet) that processes simultaneously all the audio signals acquired from the microphone in each crib and detects if the infants cried or not. The neural network takes as input Log-Mel coefficients and it is composed of stacked dilated convolutional blocks with increasing dilation factors. Each block is composed of pointwise and depthwise convolutional layers that replace standard convolutions with a mathematically equivalent but more efficient operation. The architecture has been compared to its single-channel equivalent and to single and multi-channel architectures presented in a previous work, composed of standard convolutional layers and fully-connected layers. The experiments have been conducted on a synthetic dataset that simulates the acoustic environment of the Salesi Hospital NICU located in Ancona (Italy). The results have been evaluated in terms of Area Under Precision-Recall Curve (PRC-AUC) and they showed that the proposed multi-channel Conv-DetNet achieves the highest performance with a PRC-AUC equal to 87.58%, outperforming all the comparative methods.

A recent trend in car audio systems is the generation of Individual Listening Zones (ILZ), allowing to improve phone call privacy and reduce disturbance to other passengers, without wearing headphones or earpieces. This is generally achieved by using loudspeaker arrays. In this paper, we describe an approach to achieve ILZ exploiting general purpose car loudspeakers and processing the signal through carefully designed Finite Impulse Response (FIR) filters. We propose a deep neural network approach for the design of filters coefficients in order to obtain a so-called bright zone, where the signal is clearly heard, and a dark zone, where the signal is attenuated. Additionally, the frequency response in the bright zone is constrained to be as flat as possible. Numerical experiments were performed taking the impulse responses measured with either one binaural pair or three binaural pairs for each passenger. The results in terms of attenuation and flatness prove the viability of the approach.

In recent years, a lot of effort has been put in vehicle safety systems for manned and unmanned driving. Road conditions are crucial among the factors that influence the choice of the driving style and the safety systems. A few works based the detection of the road condition on acoustic sensors mounted on the vehicle using deep learning techniques. In this work we enhance the state of the art by introducing a Siamese Convolutional Neural Network architecture able to achieve improved results for the classification of the road surface roughness. A new dataset is recorded and the approach is tested, achieving a best overall F1-score of 95.6%, improving by 14% the results of the previous method.

One of the challenges in computational acoustics is the identification of models that can simulate and predict the physical behavior of a system generating an acoustic signal. Whenever such models are used for commercial applications, an additional constraint is the time to market, making automation of the sound design process desirable. In previous works, a computational sound design approach has been proposed for the parameter estimation problem involving timbre matching by deep learning, which was applied to the synthesis of pipe organ tones. In this paper, we refine previous results by introducing the former approach in a multi-stage algorithm that also adds heuristics and a stochastic optimization method operating on perceptually motivated objective cost functions. The optimization method shows to be able to refine the first estimate given by the deep learning approach and substantially improve the objective metrics, with the additional benefit of reducing the sound design process time. Subjective listening tests are also conducted to gather additional insights on the results.

In this paper, we propose an algorithm for snoring sounds classification based on Deep Scattering Spectrum (SCAT), Gaussian Mixture Models (GMM) Supervectors and Deep Neural Networks (DNN). The task consists in the identification of the type of snoring among four target classes representing the snore sounds' excitation location, which can be highly useful for a successful medical treatment of the habitual snorer or patient afflicted with Obstructive Sleep Apnea. The SCAT is computed from excerpt the acoustic signals, then a GMM Supervector is calculated by adapting the GMM model of the acoustic space with the Maximum a Posteriori (MAP) algorithm and concatenating the mean values of the Gaussians. Resulting supervectors are used to feed the multiclass DNN classifier. The performance of the algorithm has been assessed on the Munich-Passau Snore Sound Corpus (MPSSC), composed of recordings of Drug-Induced Sleep Endoscopy (DISE) examinations. The results are expressed in terms of Unweighted Average Recall (UAR) and a remarkable improvement with respect to the state-of-the-art performance has been registered, achieving a score up to 67.14% and 67.71% respectively on the devel and test datasets.

The guitar feedback effect, or howling, is well known to the general public and identified with many rock music genres and it is the only case of acoustic feedback employed for musical purposes. Virtual Acoustic Feedback (VAF), is regarded as the extension of this phenomenon to any instrument or sound source by means of virtual acoustics and is meant to enrich the sound palette of a musician. The study of the acoustic feedback as a musical tool and computational techniques for its emulation have been scarcely addressed in literature. In this paper a nonlinear feedback oscillator is proposed and its properties derived. The oscillator does not necessarily need to be connected to a virtual instrument, thus enables to process any kind of pitched real-time input.

A plethora of different onset detection methods have been proposed in the recent years. However, few attempts have been made with respect to widely-applicable approaches in order to achieve superior performances over different types of music and with considerable temporal precision. In this paper, we present a multi-resolution approach based on discrete wavelet transform and linear prediction filtering that improves time resolution and performance of onset detection in different musical scenarios. In our approach, wavelet coefficients and forward prediction errors are combined with auditory spectral features and then processed by a bidirectional Long Short-Term Memory recurrent neural network, which acts as reduction function. The network is trained with a large database of onset data covering various genres and onset types. We compare results with state-of-the-art methods on a dataset that includes Bello, Glover and ISMIR 2004 Ballroom sets, and we conclude that our approach significantly outperforms existing methods in terms of F-Measure. For pitched non percussive music an absolute improvement of 7.5% is reported.

Computational Intelligence plays a relevant role in several Smart Grid applications, and there is a florid literature in this regard. However, most of the efforts have been oriented to the electrical energy field, for which many contributions have appeared so far, also facilitated by the availability of suitable databases to use for system training and testing. Different is the case for the water and gas scenarios: this work is thus oriented to present the state-of-the-art techniques for these grids, from 2009 to date. In particular, the focus is on load forecasting and leakage detection applications, that are the most addressed in the literature and present the biggest interest from a commercial point of view as well: the main characteristics and registered performance for all the reviewed approaches are reported. Along this direction, an extensive search of used databases has been performed and thus made available to the research community.