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2021 Journal article Open Access OPEN

Enhancing sustainability of the railway infrastructure: Trading energy saving and unavailability through efficient switch heating policies
Chiaradonna S., Masetti G., Di Giandomenico F., Righetti F., Vallati C.
Railway is currently envisioned as the most promising transportation system for both people and freight to reduce atmospheric emission and combat climate change. In this context, ensuring the energy efficiency of the railway systems is paramount in order to sustain their future expandability with minimum carbon footprint. Recent advancements in computing and communication technologies are expected to play a significant role to enable novel integrated control and management strategies in which heterogeneous data is exploited to noticeably increase energy efficiency. In this paper we focus on exploiting the convergence of heterogeneous information to improve energy efficiency of railway systems, in particular on the heating system for the railroad switches, one of the major energy intensive components. To this aim, we define new policies to efficiently manage the heating of these switches exploiting also external information such as weather and forecast data. In order to assess the performance of each strategy, a stochastic model representing the structure and operation of the railroad switch heating system and environmental conditions (both weather profiles and specific failure events) has been developed and exercised in a variety of representative scenarios. The obtained results allow to understand both strengths and limitations of each energy management policy, and serves as a useful support to make the choice of the best technique to employ to save on energy consumption, given the system conditions at hand.Source: Sustainable computing: informatics and systems (Print) 30 (2021). doi:10.1016/j.suscom.2021.100519
DOI: 10.1016/j.suscom.2021.100519

See at: ISTI Repository Open Access | Sustainable Computing Informatics and Systems Restricted | Sustainable Computing Informatics and Systems Restricted | Sustainable Computing Informatics and Systems Restricted | CNR ExploRA Restricted | Sustainable Computing Informatics and Systems Restricted | www.sciencedirect.com Restricted


2021 Journal article Open Access OPEN

On identity-aware replication in stochastic modeling for simulation-based dependability analysis of large interconnected systems
Chiaradonna S., Di Giandomenico F., Masetti G.
This paper focuses on the generation of stochastic models for dependability and performability analysis, through mechanisms for the automatic replication of template models when identity of replicas cannot be anonymous. The major objective of this work is to support the modeler in selecting the most appropriate replication mechanism, given the characteristics of the system under analysis. To this purpose, three most used solutions to identity-aware replication are considered and a formal framework to allow representing them in a consistent way is first defined. Then, a comparison of their behavior is extensively carried out, with focus on efficiency, both from a theoretical perspective and from a quantitative viewpoint. For the latter, a specific implementation of the considered replication mechanisms in the Möbius modeling environment and a case study representative of realistic interconnected infrastructures are developed.Source: Performance evaluation 147 (2021). doi:10.1016/j.peva.2021.102192
DOI: 10.1016/j.peva.2021.102192

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2020 Journal article Open Access OPEN

Computing performability measures in Markov chains by means of matrix functions
Masetti G., Robol L.
We discuss the efficient computation of performance, reliability, and availability measures for Markov chains; these metrics - and the ones obtained by combining them, are often called performability measures. We show that this computational problem can be recasted as the evaluation of a bilinear form induced by appropriate matrix functions, and thus solved by leveraging the fast methods available for this task.Source: Journal of computational and applied mathematics 368 (2020). doi:10.1016/j.cam.2019.112534
DOI: 10.1016/j.cam.2019.112534

See at: arXiv.org e-Print Archive Open Access | Journal of Computational and Applied Mathematics Open Access | ISTI Repository Open Access | Journal of Computational and Applied Mathematics Restricted | Journal of Computational and Applied Mathematics Restricted | Journal of Computational and Applied Mathematics Restricted | Journal of Computational and Applied Mathematics Restricted | CNR ExploRA Restricted | Journal of Computational and Applied Mathematics Restricted | www.sciencedirect.com Restricted | Journal of Computational and Applied Mathematics Restricted


2020 Conference article Open Access OPEN

Analyzing Forward Robustness of Feedforward Deep Neural Networks with LeakyReLU Activation Function Through Symbolic Propagation
Masetti G., Di Giandomenico F.
FeedForward Deep Neural Networks (DNNs) robustness is a relevant property to study, since it allows to establish whether the classification performed by DNNs is vulnerable to small perturbations in the provided input, and several verification approaches have been developed to assess such robustness degree. Recently, an approach has been introduced to evaluate forward robustness, based on symbolic computations and designed for the ReLU activation function. In this paper, a generalization of such a symbolic approach for the widely adopted LeakyReLU activation function is developed. A preliminary numerical campaign, briefly discussed in the paper, shows interesting results.Source: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 460–474, 14/09/2020
DOI: 10.1007/978-3-030-65965-3_31

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2020 Conference article Open Access OPEN

Trading dependability and energy consumption in critical infrastructures: Focus on the rail switch heating system
Chiaradonna S., Di Giandomenico F., Masetti G.
Traditionally, critical infrastructures demand for high dependability, being the services they provide essential to human beings and the society at large. However, more recent attention to cautious usage of energy resources is changing this vision and calls for solutions accounting for appropriate multi-requirements combinations when developing a critical infrastructure. In such a context, analysis supports able to assist the designer in envisioning a satisfactory trade-off among the multi-requirements for the system at hand are highly helpful. In this paper, the focus is on the railway sector and the contribution is a stochastic model-based analysis framework to quantitatively assess trade-offs between dependability indicators and electrical energy consumption incurred by the rail switch heating system.Moving from a preliminary study that concentrated on energy consumption only, the analysis framework has been extended to become a solid support to devise appropriate tuning of the heating policy that guarantees satisfactory trade-offs between dependability and energy consumption. An evaluation campaign in a variety of climate scenarios demonstrates the feasibility and utility of the developed framework.Source: 25th IEEE Pacific Rim International Symposium on Dependable Computing, pp. 150–159, Perth, Australia, 01/12/2021
DOI: 10.1109/prdc50213.2020.00026

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2020 Contribution to conference Open Access OPEN

Enhancing sustainability of the railway infrastructure through efficient energy management policies
Chiaradonna S., Masetti G., Di Giandomenico F., Righetti F., Vallati C.
Railway is currently envisioned as the most promising transportation system for both people and freight to reduce atmospheric emission and combat climate change. In this context, ensuring the energy efficiency of the railway systems is paramount in order to sustain their future expandability with minimum carbon footprint. Recent advancements in computing and communication technologies are expected to play a significant role to enable novel integrated control and management strategies in which heterogeneous data is exploited to noticeably increase energy efficiency. In this paper we focus on exploiting the convergence of heterogeneous information to improve energy efficiency of railway systems, in particular on the heating system for the railroad switches, one of the major energy intensive components. To this aim, we define new policies to efficiently manage the heating of these switches exploiting also external information such as weather and forecast data. In order to assess the performance of each strategy, a stochastic model representing the structure and operation of the railroad switch heating system and environmental conditions (both weather profiles and specific failure events) has been developed and exercised in a variety of representative scenarios. The obtained results allow to understand both strengths and limitations of each energy management policy, and serves as a useful support to make the choice of the best technique to employ to save on energy consumption, given the system conditions at hand.Source: 11th International Green and Sustainable Computing Conference, Virtual Conference, 19/10/2020, 22/10/2020

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2020 Conference article Open Access OPEN

Failure management strategies for IoT-based railways systems
Righetti F., Vallati C., Anastasi G., Masetti G., Di Giandomenico F.
Railways monitoring and control are currently performed by different heterogeneous vertical systems working in isolation without or with limited cooperation among them. Such configuration, widely adopted in practical deployments today, is in contrast with the integrated vision of systems that are at the foundation of the smart-city concept. In order to overcome the current fractured ecosystem that monitors and controls railways functionalities, the adoption of a novel integrated approach is mandatory to create an all-in-one railway system. To this aim, new IoT-based communication technologies, like wireless or Power Line Communication technologies, are considered the main enablers to integrate in a very rapid and easy manner existing vertical systems. In this work, we analyse the architecture of future railways systems based on a mix of wireless and Power Line Communication technologies. In our analysis, we aim at studying possible failure management strategies on rail-road switches to improve the level of reliability, crucial requirement for systems that demand maximum resiliency as they manage a critical function of the infrastructure. In particular, we propose a set of solutions aimed at detecting and handling network and sensor failures to ensure continuity in the execution of the basic control functions. The proposed approach is evaluated by means of simulations and demonstrated to be effective in ensuring a good level of performance even when failures occur.Source: 2020 IEEE International Conference on Smart Computing, pp. 386–391, Bologna, 14-17/09/2020
DOI: 10.1109/smartcomp50058.2020.00082

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2020 Journal article Open Access OPEN

Analysis of violin combination tones and their contribution to Tartini's third tone
Caselli G., Cecchi G., Malacarne M., Masetti G.
It is widely accepted that the famous Tartini's third tone, i.e., the appearance of an additional third tone of lower frequency when playing a dyad on the violin, is a subjective phenomenon generated by the listener's cochlear nonlinearity. However, the recent demonstration that additional tones of audible amplitude can also be generated by the violin itself during playing of a dyad (violin combination tones), raises the question if these tones might have influenced Tartini's third sound perception. The experiments reported here were made to ascertain this possibility. To this end, following Tartini experiments, several dyads played by either one violin or two violins playing one note of the dyad each, were recorded. The analysis of the spectra shows that violin combination tones are present in all the dyads investigated, but exclusively when the dyad is played by a single violin and not when the same dyad is played by two violins. Tartini found the third tones to be the same in both conditions, which means that violin combination tones in his experiments were either absent or too small to affect the perception of the subjective third tones arising from cochlear distortion.Source: Savart journal 1 (2020).

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2019 Report Open Access OPEN

Tensor methods for the computation of MTTF in large systems of loosely interconnected components
Masetti G., Robol L.
We are concerned with the computation of the mean-time-to-failure(MTTF) for a large system of loosely interconnected components, mod-eled as continuous time Markov chains. In particular, we show that split-ting the local and synchronization transitions of the smaller subsystemsallows to formulate an algorithm for the computation of the MTTF whichis proven to be linearly convergent. Then, we show how to modify themethod to make it quadratically convergent, thus overcoming the difficul-ties for problems with convergent rate close to1.In addition, it is shown that this decoupling of local and synchroniza-tion transitions allows to easily represent all the matrices and vectors in-volved in the method in the tensor-train (TT) format -- and we providenumerical evidence showing that this allows to treat large problems withup to billions of states -- which would otherwise be unfeasible.Source: ISTI Technical reports, 2019

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2019 Conference article Open Access OPEN

Distinguishing Violinists and Pianists Based on Their Brain Signals
Coro G., Masetti G., Bonhoeffer P., Betcher M.
Many studies in neuropsychology have highlighted that expert musicians, who started learning music in childhood, present structural differences in their brains with respect to non-musicians. This indicates that early music learning affects the development of the brain. Also, musicians' neuronal activity is different depending on the played instrument and on the expertise. This difference can be analysed by processing electroencephalographic (EEG) signals through Artificial Intelligence models. This paper explores the feasibility to build an automatic model that distinguishes violinists from pianists based only on their brain signals. To this aim, EEG signals of violinists and pianists are recorded while they play classical music pieces and an Artificial Neural Network is trained through a cloud computing platform to build a binary classifier of segments of these signals. Our model has the best classification performance on 20 seconds EEG segments, but this performance depends on the involved musicians' expertise. Also, the brain signals of a cellist are demonstrated to be more similar to violinists' signals than to pianists' signals. In summary, this paper demonstrates that distinctive information is present in the two types of musicians' brain signals, and that this information can be detected even by an automatic model working with a basic EEG equipment.Source: ICANN 2019: Theoretical Neural Computation 28th International Conference on Artificial Neural Networks, pp. 123–137, Monaco di Baviera, 17-19/10/2019
DOI: 10.1007/978-3-030-30487-4_11

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2019 Journal article Open Access OPEN

On Extending and Comparing Newton-Raphson Variants for Solving Power-Flow Equations
Dutto S., Masetti G., Chiaradonna S., Di Giandomenico F.
This paper focuses on power-flow equations solutions, based on the Newton-Raphson method. Two major contributions are offered. First, the definition of novel solution variants, resorting to Wirtinger calculus, is attempted. The obtained developments, although original in their formulation, led to already known variants. Despite the impaired originality of the obtained solution, there are significant lessons learned from such an effort. The second major contribution consists of a deep comparison analysis of existing solution strategies, based on complex and real variables, and the Wirtinger based ones, all properly reformulated to allow direct comparison with each other. The goal is to investigate strengths and weaknesses of the addressed techniques in terms of computational effort and convergence rate, which are the most relevant aspects to consider while choosing the approach to employ to solve power-flow equations for a specific power system under study.Source: IEEE transactions on power systems 34 (2019): 2577–2587. doi:10.1109/TPWRS.2019.2897640
DOI: 10.1109/tpwrs.2019.2897640

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2019 Conference article Open Access OPEN

Stochastic modeling and evaluation of large interdependent composed models through Kronecker algebra and exponential sums
Masetti G., Robol L., Chiaradonna S., Di Giandomenico F.
The KAES methodology for efficient evaluation of dependability-related properties is proposed. KAES targets systems representable by Stochastic Petri Nets-based models, composed by a large number of submodels where interconnections are managed through synchronization at action level. The core of KAES is a new numerical solution of the underlying CTMC process, based on powerful mathematical techniques, including Kronecker algebra, Tensor Trains and Exponential Sums. Specifically, advancing on existing literature, KAES addresses efficient evaluation of the Mean-Time-To-Absorption in CTMC with absorbing states, exploiting the basic idea to further pursue the symbolic representation of the elements involved in the evaluation process, so to better cope with the problem of state explosion. As a result, computation efficiency is improved, especially when the submodels are loosely interconnected and have small number of states. An instrumental case study is adopted, to show the feasibility of KAES, in particular from memory consumption point of view.Source: The 40th International Conference on Application and Theory of Petri Nets and Concurrency, pp. 47–66, Berlin, 23-28/06/2019
DOI: 10.1007/978-3-030-21571-2_3

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2019 Contribution to book Open Access OPEN

A Refined Framework for Model-Based Assessment of Energy Consumption in the Railway Sector
Chiaradonna S., Di Giandomenico F., Masetti G., Basile D.
Awareness and efforts to moderate energy consumption, desirable from both economical and environmental perspectives, are nowadays increasingly pursued. However, when critical sectors are addressed, energy saving should be cautiously tackled, so to not impair stringent dependability properties such contexts typically require. This is the case of the railway transportation system, which is the critical infrastructure this paper focuses on. For this system category, the attitude has been typically to neglect efficient usage of energy sources, motivated by avoiding to put dependability in danger. The new directives, both at national and international level, are going to change this way of thinking. Our study intends to be a useful support to careful energy consumption. In particular, a refined stochastic modeling framework is offered, tailored to the railroad switch heating system, through which analyses can be performed to understand the sophisticated dynamics between the system (both the cyber and physical components) and the surrounding weather conditions.Source: From Software Engineering to Formal Methods and Tools, and Back. Essays Dedicated to Stefania Gnesi on the Occasion of Her 65th Birthday, edited by Maurice H. ter Beek, Alessandro Fantechi, Laura Semini, pp. 481–501, 2019
DOI: 10.1007/978-3-030-30985-5_28

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2019 Doctoral thesis Open Access OPEN

Enhanced power grid evaluation through efficient stochastic model-based analysis
Masetti G.
The electrical infrastructure can be considered nowadays as a meta-critical infrastructure: in fact it is the basis for almost all the critical infrastructures a modern nation can have, such as water, oil, gas and transportation. This implies that its correct operation is a fundamental requirement to the correct operation of the critical infrastructures that depend on it. To allow pervasive control and monitoring towards resilience and performance enhancements, the Smart Grid is emerging as a convergence of information and commu- nication technology with power system engineering. In particular, the ever increasing level of distributed energy resources penetration calls for more and more sophisticated monitoring and control facilities. So, studying the influence of distributed energy resources, of new control policies and ICT on the dependability of distribution grids offers valuable insights on how to improve the design of Smart Grids. In addition to standard dependability measures such as reliability and availability, among greatly relevant measures specifically defined for electrical distribution systems there are the voltage quality and the energy required, but not supplied, by the distribution system. A popular approach to assess electrical distribution specific measures, in presence of failures or attacks to the ICT system and/or to the electric in- frastructure, is the stochastic model-based analysis. Although several studies have been already proposed, the research in this context still faces a number of challenges, mainly due to the need: i) to consider both the ICT sub- system and the controlled electrical infrastructure, to properly account for (inter)dependencies through which operations (and failures/attacks) propa- gate; ii) to model and analyze the SG components at a sufficiently detailed level of abstraction, targeting realistic representation of their structure and behavior in view of promoting accuracy of the assessment itself. Both nomi- nal and a variety of faulty behaviors are to be investigated, since the interest is on assessing resilience and quality related attributes; iii) to tackle realistic segments of SG in terms of topology size, to make the evaluation study of real interest to stakeholders involved in the field. To cope with all these needs results in huge and complex models, to be typically defined in a modular fashion and requiring sophisticated composi- tional operators. Moreover, model solution through simulation-based eval- uation becomes unavoidable in presence of non-Markovian behavior of the involved components, thus preventing the use of analytical approaches. Given the above premises, the stochastic model-based analysis of realistic SG topologies is a research area where further investigations and enhance- ments are highly desirable. In this context, this thesis offers contributions in the direction of promoting efficient evaluation of SG in realistic scenarios from a resilience perspective.

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2018 Report Open Access OPEN

Analyzing a security and reliability model using Krylov methods and matrix functions
Masetti G., Robol L.
It has been recently shown how the computation of performability measures for Markov models can be recasted as the evaluation of a bilinear forminduced by appropriate matrix functions. In view of these results, we show how to analyze a security model, inspired by a real world scenario. The model describes a mobile cyber-physical system of communicating nodes which are subject to security attacks. We take advantage of the properties of matrix functions of block matrices, and provide efficient evaluation methods.Moreover, we show how this new formulation can be used to retrieve interesting theoretical results, which can also rephrased in probabilistic terms.Source: ISTI Technical reports, 2018

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2018 Contribution to conference Open Access OPEN

The role of groundwater modelling for the sustainable management of water resources in a context of climatic change: an experience on a carbonate aquifer in Tuscany (Italy)
Scozzari A., Doveri M., Masetti G., Menichini M., Provenzale A., Raco B., Vivaldo G.
In a growing number of countries, safeguarding drinking water supplies is strictly linked with the sustainable usage of groundwater resources. In the European Union, about 70% of the water destined to the supply network is groundwater, and almost 75% of this source comes from carbonate aquifers. Although groundwater systems can be considered as more resilient to climate change than surface waters, climate change affects them both directly and indirectly. For carbonate aquifers the impact can be very significant, given the high sensitivity of these reservoirs.caused by their karst features. The analysis of hydro-meteorological data over a few decades highlights that also Italy is experiencing a change in the climate regime, with impacts on groundwater yield that are not yet well understood. In this work, we discuss the results of the analysis of data provided by the Tuscan Water Authority (AIT) and GAIA SpA (Integrated Water Service). Data refer to flowrate at springs of the karst aquifer system of the Apuan Alps (northwestern Tuscany). Flowrates trend indicates a slight decline of groundwater yields in this system over the last two decades. A tendency to consume more recharge water through sudden and short flow rate peaks seems also to occur, as a consequence of the increased occurrence of storm events. Data were elaborated in order to study possible empirical relationships between meteorological parameters and groundwater quantity indices, in the wider framework of a research for the development of support tools for the management of the resource under specific climate scenarios. In particular, this work describes the different data-driven approaches experimented with the collected time series, essentially based on multi-variate analysis techniques and on a simplified machine learning scheme based on neural networks. The collected time series were first analyzed by classical statistical and advanced spectral analysis techniques, in order to extract the embedded significant periodicities and trends. Forecasting was thus applied on clean signals only, to reduce the background noise propagation; both empirical models applied to the whole cleaned dataset, and single components projection methodologies were taken into account.Source: EGU General Assembly 2018, Vienna, Austria, 8-13 April 2018

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2018 Conference article Open Access OPEN

An efficient strategy for model composition in the Möbius modeling environment
Masetti G., Chiaradonna S., Di Giandomenico F., Feddersen B., Sanders W. H.
Möbius is well known as a modeling and evaluation environment for performance and dependability indicators. One of Möbius' key features is the modular and compositional approach to model definition and analysis. In particular, the modeler can define submodels using several formalisms and compose them to form the overall model of the system under analysis. The current algorithm for model composition in Möbius revealed performance issues when large systems are considered (such as in the modeling of realistic segments of energy or transportation infrastructures), due to the chosen data flow scheme. In this paper, a new algorithm for the same composition mechanism is proposed to improve efficiency. A case study is also developed to demonstrate the performance enhancements.Source: 14th European Dependable Computing Conference 2018, pp. 116–119, Iasi, Romania, 10-14 September, 2018
DOI: 10.1109/edcc.2018.00029

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2018 Conference article Open Access OPEN

Supporting CPS modeling through a new method for solving complex non-holomorphic equations
Masetti G., Dutto S., Chiaradonna S., Di Giandomenico F.
Modeling cyber-physical systems (CPSs) for assessment or design support purposes is a complex activity. Capturing all relevant physical, structural or behavioral aspects of the system at hand is a crucial task, which often implies representation of peculiar features/constraints through non-linear equations. Values that fulfill the constraints, described with a domain specific language, are obtained solving the equations through a properly developed solution tool. Only for a limited set of CPSs it is possible to find a straightforward strategy to design the software that solves the constraints equations. In the general case, instead, the modeler has to develop an ad-hoc artifact for each different system. This is the case of non-holomorphic but real analytic complex equations, adopted to represent system components with wave behaviors. In this paper, we present a new approach to develop a software for solving such complex equations following a generative programming strategy, based on Wirtinger derivatives within the Newton-Raphson method.Source: 6th International Conference on Model-Driven Engineering and Software Development, MODELSWARD, pp. 680–688, Madeira, Portugal, 22-24 January, 2018
DOI: 10.5220/0006752306800688

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2018 Conference article Restricted

Enhanced dependability evaluation through Krylov methods and matrix functions: the case of load-sharing systems
Masetti G.
Recently, the link between performance and dependability measures for Markovian models and the evaluation of bilinear forms induced by well-known matrix functions has been established. The connection can be exploited to obtain effective and efficient solution methods for allowing in particular the computation of reliability-related measures. In this paper, a reliability model for a load-sharing system is discussed and then solved through Krylov methods generated by the mentioned connection.Source: 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W 2018), pp. 92–95, Luxembourg City, Luxembourg, 25-28/06/2018
DOI: 10.1109/dsn-w.2018.00043

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2018 Journal article Open Access OPEN

Tartini, the third tone and the cochlea
Caselli G., Masetti G., Cecchi G.
"It is physically certain that given two simultaneous loud and prolonged tones one can hear a third tone, different from the played sounds": with these words taken from his essay Trattato di musica secondo la vera scienza dell'armonia published in 1754 Giuseppe Tartini (1692-1770), celebrated violinist, and researcher of musical theory and "physicomathematical harmonist" (Barbieri, 1990) introduces his discovery, made some years before (in 1713), of the socalled third tone. As explained by Tartini himself, if a listener stands halfway between two violin or oboe players some steps apart, he can hear another tone, in addition to the played notes (principal or primary notes), called by Tartini the "third tone"Source: Ph (Milano) (2018).

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