1,467 research outputs found
Cultural Initiatives for Sustainable Development. Management, Participation and Entrepreneurship in the Cultural and Creative Sector
No full textTop-Down Attention Modelling in a Cocktail Party Scenario
No full textComputational auditory scene analysis (CASA) focuses on the problem of building machines able to understand and interpret complex acoustic scenarios and react, after a brief period, in an opportune way. A complex acoustic scenario can be characterized by several sounds of various origin and nature coming from different sources. Consequently, one of the main challenges is the simultaneous elaboration of all this information with limited computational resources.
Colin Cherry, in1953, investigated human behavior in the same circumstances, (which he called “cocktail party problem”). He performed several experiments proving that people are very efficient cocktail party solvers, making use of attentive mechanisms.
Attentive mechanisms, in fact, allow the brain to focus on what it is necessary to follow and ignore what it is possible to discard. This selection procedure is driven by many factors; depending on the nature of these factors, it is possible to distinguish between a bottom-up and top-down perspective. In the first case, sounds of interest are those which stand out from the scene, without involving a real attentive processing, but just the pre-attentive one. In the second case, the goal, a particular task, the previous decisions and the acquired models guide the subjects' attention. The fusion of these two modalities suggests to the brain what is salient and what can be attenuated or deleted.
In this thesis, we propose a top-down attention model and we carry out behavioral experiments –inspired by the Cherry’s ones- to investigate the role of top-down attention in the cocktail party. In particular, we model top-down attention as a sequential decision making process driven by a task – modeled as a classification problem - in an environment with random subsets of features missing, but where we have the possibility to gather additional features among the ones that are missing. Thus, the top-down attention problem is reduced to finding the answer to the question what to measure next? Attention is based on the top-down saliency of the missing features given as the estimated difference in classification confusion (entropy) with and without the given feature. The difference in confusion is computed conditioned on the available set of features. We also investigated missing data problem, comparing the efficiency of some
missing data techniques and used the results to make our attention model more realistic by also allowing the initial training phase to take place with incomplete data. Moreover, we simulate the cocktail party problem in the model and make predictions about sensitivity to confounders under different levels of attention. We finally examine the role of temporal and spectral overlaps for human speech intelligibility, and how the presence of a task influences it.
We also investigated multi-modal human-robot interaction and proposed a multimodal speaker identification system, combining acoustic and visual features to identify and track people taking part in a conversation.This thesis is the result of an informal joint Ph.D. between Sapienza, University of
Rome and DTU, Technical University of Denmark. I spent the first half of my Ph.D.
period in Italy, working under the supervision of Prof. Fiora Pirri and the other half
in Denmark, working under the supervision of Prof. Lars Kai Hansen.
Unfortunately, because of Danish legislation at the time I started, it was not possible
to have the formal agreement between the two universities
Marina Marchegiani (1974-2018)
No full textFil: Spano, Romina Clara. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad de Buenos Aires. Instituto de las Culturas; ArgentinaFil: Palamarczuk, Valeria. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad de Buenos Aires. Instituto de las Culturas; ArgentinaFil: Reynoso, Alejandra Daniela. Universidad de Buenos Aires. Facultad de Filosofía y Letras. Museo Etnográfico "Juan B. Ambrosetti"; ArgentinaFil: Estévez, Juan Manuel. Universidad de Buenos Aires. Facultad de Filosofía y Letras. Museo Etnográfico "Juan B. Ambrosetti"; ArgentinaSpano, R. C., Palamarczuk, V., Reynoso, A. D. y Estevez, J. M. (2019). Marina Marchegiani (1974-2018). Arqueología, 25(2), 293-298
Learning to listen to your ego-(motion): Metric motion estimation from auditory signals
No full textThis paper is about robot ego-motion estimation relying solely on acoustic sensing. By equipping a robot with microphones, we investigate the possibility of employing the noise generated by the motors and actuators of the vehicle to estimate its motion. Audio-based odometry is not affected by the scene’s appearance, lighting conditions, and structure. This makes sound a compelling auxiliary source of information for ego-motion modelling in environments where more traditional methods, such as those based on visual or laser odometry, are particularly challenged. By leveraging multi-task learning and deep architectures, we provide a regression framework able to estimate the linear and the angular velocity at which the robot has been travelling. Our experimental evaluation conducted on approximately two hours of data collected with an unmanned outdoor field robot demonstrated an absolute error lower than 0:07 m/s and 0:02 rad/ss for the linear and angular velocity, respectively. When compared to a baseline approach, making use of single-task learning scheme, our system shows an improvement of up to 26% in the ego-motion estimation
Marina Marchegiani (1974-2018)
No full textFil: Spano, Romina Clara. Consejo Nacional de Investigaciones Cient�ficas y T�cnicas. Centro Regional de Investigaciones Cient�ficas y Transferencia Tecnol�gica de La Rioja; Argentina. Consejo Nacional de Investigaciones Cient�ficas y T�cnicas. Universidad de Buenos Aires. Instituto de las Culturas; ArgentinaFil: Palamarczuk, Valeria. Consejo Nacional de Investigaciones Cient�ficas y T�cnicas. Universidad de Buenos Aires. Instituto de las Culturas; ArgentinaFil: Reynoso, Alejandra Daniela. Universidad de Buenos Aires. Facultad de Filosof�a y Letras. Museo Etnogr�fico "Juan B. Ambrosetti"; ArgentinaFil: Est�vez, Juan Manuel. Universidad de Buenos Aires. Facultad de Filosof�a y Letras. Museo Etnogr�fico "Juan B. Ambrosetti"; ArgentinaSpano, R. C., Palamarczuk, V., Reynoso, A. D. y Estevez, J. M. (2019). Marina Marchegiani (1974-2018).�Arqueolog�a,�25(2), 293-298
Marina Marchegiani (1974-2018)
No full textFil: Spano, Romina Clara. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad de Buenos Aires. Instituto de las Culturas; ArgentinaFil: Palamarczuk, Valeria. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad de Buenos Aires. Instituto de las Culturas; ArgentinaFil: Reynoso, Alejandra Daniela. Universidad de Buenos Aires. Facultad de Filosofía y Letras. Museo Etnográfico "Juan B. Ambrosetti"; ArgentinaFil: Estévez, Juan Manuel. Universidad de Buenos Aires. Facultad de Filosofía y Letras. Museo Etnográfico "Juan B. Ambrosetti"; ArgentinaSpano, R. C., Palamarczuk, V., Reynoso, A. D. y Estevez, J. M. (2019). Marina Marchegiani (1974-2018). Arqueología, 25(2), 293-298
From Mecenatism to crowdfunding: engagement and identification in cultural-creative projects
No full textThe aim of the research is to analyse the behaviours of potential micro-investors and their willingness to participate in crowdfunding campaigns to sustain cultural and creative projects. The author proposes a quantitative approach, through a first exploratory survey and second an experimental design. This experiment designed alternative crowdfunding schemes and posted them online to solicit behaviours by the potential crowdfunders. The vast majority of respondents would contribute to projects with minimum amounts of crowdfunding, whatever the economic model adopted, or even not bring any financial contribution. This methodology puts in evidence that content variables and context variables are important. The results show that crowdfunding is a viable model to overcome the public expenditure liabilities related to the valorization of cultural heritage, given that specific conditions are satisfied. First, risk perceptions must be mitigated with full disclosure of information. Second, two clusters of potential crowdfunders can be engaged using different approaches
L’accordo tra l’Unione europea e la Turchia per la gestione dei flussi migratori: cronaca di una morte annunciata?
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