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May 19 2015

cs.SD arXiv:1505.04385v1

This paper proposes an efficient parameterization of the Room Transfer Function (RTF). Typically, the RTF rapidly varies with varying source and receiver positions, hence requires an impractical number of point to point measurements to characterize a given room. Therefore, we derive a novel RTF parameterization that is robust to both receiver and source variations with the following salient features: (i) The parameterization is given in terms of a modal expansion of 3D basis functions. (ii) The aforementioned modal expansion can be truncated at a finite number of modes given that the source and receiver locations are from two sizeable spatial regions, which are arbitrarily distributed. (iii) The parameter weights/coefficients are independent of the source/receiver positions. Therefore, a finite set of coefficients is shown to be capable of accurately calculating the RTF between any two arbitrary points from a predefined spatial region where the source(s) lie and a pre-defined spatial region where the receiver(s) lie. A practical method to measure the RTF coefficients is also provided, which only requires a single microphone unit and a single loudspeaker unit, given that the room characteristics remain stationary over time. The accuracy of the above parameterization is verified using appropriate simulation examples.

In our work we analyse the political disaffection or "the subjective feeling of powerlessness, cynicism, and lack of confidence in the political process, politicians, and democratic institutions, but with no questioning of the political regime" by exploiting Twitter data through machine learning techniques. In order to validate the quality of the time-series generated by the Twitter data, we highlight the relations of these data with political disaffection as measured by means of public opinion surveys. Moreover, we show that important political news of Italian newspapers are often correlated with the highest peaks of the produced time-series.