Fabio Antonacci

Politecnico di Milano

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Augusto Sarti

Politecnico di Milano

185

Stefano Tubaro

Politecnico di Milano

Mirco Pezzoli

Politecnico di Milano

Antonio Canclini

Politecnico di Milano

Luca Comanducci

Politecnico di Milano

Sebastián González

Politecnico di Milano

Alberto Bernardini

Politecnico di Milano

A. Sarti

Istituto Nazionale di Fisica Nucleare, Sezione di Roma I

Federico Borra

Politecnico di Milano

Paolo Bestagini

Politecnico di Milano

Cooperative Institutions

Politecnico di Milano

Institute of Electrical and Electronics Engineers

University of Catania

Centre National de la Recherche Scientifique

National Institute for Astrophysics

Consorzio di Bioingegneria e Informatica Medica

Institute for Space Astrophysics and Planetology

University of Waikato

National Institute of Standards

Sapienza University of Rome

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Ray space analysis with sparse recovery

Craig Jin Fabio Antonacci Augusto Sarti

This work explores integrating sparse recovery methods into the ray space transform. Sparse recovery methods have proven useful in microphone array analysis of sound fields. In particular, they can provide extremely accurate estimates of source direction in the presences of multiple, simultaneous sources and noise. The ray space transform has recently emerged as a useful tool to analyse sound fields, particulary by robustly integrating information from multiple viewpoints. In this work, we present the results of numerical simulations for a linear microphone array that demonstrate the promising improvements obtained by integrating sparse recovery into the ray space transform.

Viewpoints

Sparse array

10.1109/waspaa.2017.8170031

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Citations (3)

Using Mechanical Metamaterials in Guitar Top Plates: A Numerical Study

Applied Sciences (2022)

Mattia Lercari Sebastián González Carolina Espinoza Giacomo Longo Fabio Antonacci

It has recently been shown that the mechanical properties of thin, rectangular wooden plates can be tuned by carving them with specific patterns of perforations, effectively realising a 2D wooden mechanical metamaterial. Such a material is of great interest for the construction of musical instruments, as it could allow a new degree of creative control for makers. Furthermore, issues with the shrinking supplies of tone-woods could be alleviated as wood samples that don not meet the desired requirements could simply be altered, instead of being discarded. In this work, we study the effect of the use of these metamaterials in the soundboards of classical guitars. By way of simulations, we evaluate their impact on the modal behaviour and on the sound pressure level of the instrument, as well as on its ability to sustain the load exerted by the strings. Our results show that the metamaterials can tune the instrument’s response without compromising its structural integrity. We thus conclude that the use of wooden mechanical metamaterials in the soundboards of classical guitars is feasible and, in many ways, beneficial, not the least since it opens the door to using non-traditional woods with bespoke density and stiffness.

Guitar

Carving

10.3390/app12178619

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Hyperbolic boiler tube leak location based on quaternary acoustic array

Applied Thermal Engineering (2011)

Liansuo An Peng Wang Augusto Sarti Fabio Antonacci Jie Shi

Leak Detection

10.1016/j.applthermaleng.2011.06.028

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Citations (18)

REPRESENTATION AND CONTROL STRATEGIES FOR LARGE KNOWLEDGE DOMAINS: An Application to NLP

Applied Artificial Intelligence (1988)

Fabio Antonacci Marina Russo Maria Teresa Pazienza Paola Velardi

The design issues encountered during the development of a natural language processor (NLP) for the Italian language are described. The focus is on strategic aspects, namely representation and control, and their implementation with first-order logic. The complexity and the size of the knowledge domain (press agency releases on finance and economics) do not present severe restrictions in the sentence structure; hence a considerable design effort for data structures and control algorithms was required. Logic proved to be an important tool for implementing in a modular and efficient way the knowledge sources along with the programs that derive the morphologic, syntactic, and semantic features of sentences. As for the data structures, we found a considerable advantage in separating linguistic knowledge in three sources: morphologic, syntactic, and semantic. This resulted in a clear and systematic representation scheme and reduced the complexity of the parsing system.

Representation

10.1080/08839518808949909

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Citations (9)

Wave Digital Implementation of Robust First-Order Differential Microphone Arrays

IEEE Signal Processing Letters (2017)

Alberto Bernardini Fabio Antonacci Augusto Sarti

In this letter, a novel time-domain implementation of robust first-order differential microphone arrays (DMAs), based on wave digital filters, is presented. The proposed beamforming method is extremely efficient, as it requires at most two multipliers and one delay for each filter, where the necessary number of filters equals the number of physical microphones of the array, and it avoids the use of fractional delays. The update of the coefficients of the filters, required for reshaping the beampattern, has a significantly lower computational cost with respect to the time-domain methods presented in the literature. This makes the proposed method suitable for real-time DMA applications with time-varying beampatterns.

10.1109/lsp.2017.2787484

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On the Statistical Model of Source Localization based on Range Difference Measurements

arXiv (Cornell University) (2016)

Marco Antônio Compagnoni Roberto Notari Fabio Antonacci A. Sarti

In this work we study the statistical model of source localization based on Range Difference measurements. We investigate the case of planar localization of a source using a minimal configuration of three non aligned receivers. Our analysis is based on a previous work of the same authors concerning the localization in a noiseless scenario. As the set of feasible measurements is a semialgebraic variety, this investigation makes use of techniques from Algebraic Statistics and Information Geometry.

Statistical Analysis

10.48550/arxiv.1606.08303

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Source Localization Using Distributed Microphones in Reverberant Environments Based on Deep Learning and Ray Space Transform

IEEE/ACM Transactions on Audio Speech and Language Processing (2020)

Luca Comanducci Federico Borra Paolo Bestagini Fabio Antonacci Stefano Tubaro

In this article we present a methodology for source localization in reverberant environments from Generalized Cross Correlations (GCCs) computed between spatially distributed individual microphones. Reverberation tends to negatively affect localization based on Time Differences of Arrival (TDOAs), which become inaccurate due to the presence of spurious peaks in the GCC. We therefore adopt a data-driven approach based on a convolutional neural network, which, using the GCCs as input, estimates the source location in two steps. It first computes the Ray Space Transform (RST) from multiple arrays. The RST is a convenient representation of the acoustic rays impinging on the array in a parametric space, called Ray Space. Rays produced by a source are visualized in the RST as patterns, whose position is uniquely related to the source location. The second step consists of estimating the source location through a nonlinear fitting, which estimates the coordinates that best approximate the RST pattern obtained through the first step. It is worth noting that training can be accomplished on simulated data only, thus relaxing the need of actually deploying microphone arrays in the acoustic scene. The localization accuracy of the proposed techniques is similar to the one of SRP-PHAT, however our method demonstrates an increased robustness regarding different distributed microphones configurations. Moreover, the use of the RST as an intermediate representation makes it possible for the network to generalize to data unseen during training.

Spurious relationship

Robustness

Representation

10.1109/taslp.2020.3011256

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Citations (20)

Physics-Informed Neural Network for Volumetric Sound field Reconstruction of Speech Signals

arXiv (Cornell University) (2024)

Marco Olivieri Xenofon Karakonstantis Mirco Pezzoli Fabio Antonacci Augusto Sarti

Recent developments in acoustic signal processing have seen the integration of deep learning methodologies, alongside the continued prominence of classical wave expansion-based approaches, particularly in sound field reconstruction. Physics-Informed Neural Networks (PINNs) have emerged as a novel framework, bridging the gap between data-driven and model-based techniques for addressing physical phenomena governed by partial differential equations. This paper introduces a PINN-based approach for the recovery of arbitrary volumetric acoustic fields. The network incorporates the wave equation to impose a regularization on signal reconstruction in the time domain. This methodology enables the network to learn the underlying physics of sound propagation and allows for the complete characterization of the sound field based on a limited set of observations. The proposed method's efficacy is validated through experiments involving speech signals in a real-world environment, considering varying numbers of available measurements. Moreover, a comparative analysis is undertaken against state-of-the-art frequency-domain and time-domain reconstruction methods from existing literature, highlighting the increased accuracy across the various measurement configurations.

10.48550/arxiv.2403.09524

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A Fast Ray Space Transform for Wave Field Processing using Acoustic Arrays

2021 29th European Signal Processing Conference (EUSIPCO) (2020)

Federico Borra Mirco Pezzoli Luca Comanducci Alberto Bernardini Fabio Antonacci

The importance of soundfield imaging techniques is expected to further increase in the next few years thanks to the ever-increasing availability of low-cost sensors such as MEMS microphones. When it comes to processing a relevant number of sensor signals, however, the computational load of space-time processing algorithms easily grows to unmanageable levels. The Ray Space Transform (RST) was recently introduced as a promising tool for soundfield analysis. Given the collection of signals captured by a uniform linear array of microphones, the RST allows us to collect and map the directional components of the acoustic field onto a domain called "ray space", where relevant acoustic objects are represented as linear patterns for advanced acoustic analysis and synthesis applications. So far the computational complexity of the RST linearly increases with the number of microphones. In order to alleviate this problem, in this paper we propose an alternative efficient implementation of the RST based on the Non Uniform Fast Fourier Transform.

10.23919/eusipco47968.2020.9287380

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Citations (4)

Synthesizing Soundscapes: Leveraging Text-to-Audio Models for Environmental Sound Classification

arXiv (Cornell University) (2024)

Francesca Ronchini Luca Comanducci Fabio Antonacci

In the past few years, text-to-audio models have emerged as a significant advancement in automatic audio generation. Although they represent impressive technological progress, the effectiveness of their use in the development of audio applications remains uncertain. This paper aims to investigate these aspects, specifically focusing on the task of classification of environmental sounds. This study analyzes the performance of two different environmental classification systems when data generated from text-to-audio models is used for training. Two cases are considered: a) when the training dataset is augmented by data coming from two different text-to-audio models; and b) when the training dataset consists solely of synthetic audio generated. In both cases, the performance of the classification task is tested on real data. Results indicate that text-to-audio models are effective for dataset augmentation, whereas the performance of the models drops when relying on only generated audio.

Soundscape

10.48550/arxiv.2403.17864

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