Comparison of the seismic behaviour between three building tests, all based on a two storey model house

Integral Masonry System consisting of intersecting steel trusses alo ng each of the three dimensional directions of space on walls and slabs using any masonry material, had yet been backed up by the previous adobe test for seismic areas. This paper presents the comparison this last test and the adaptation of the IMS using h ollow brick. A prototype based on a two storey model house (6mx6mx6m) has being also built in two different scales in order to maximize the load and size of the shake table: the first one half size the whole building (3mx3mx3m) and the second, a quarter of the real size (3mx3mx6m). Both tests have suffered some mild to moderate damages while supporting the higher seismic action subjected by the shake table, without even fissuring the first test and with very few damages the second one. The thickness of the hollow brick wall and the diameter of the tree - dimensional truss reinforcement were scaled to the real size test in order to ascertain its great structural behaviour in relation to the previous structural model calculations. The aim of this study is to sum marize the results of the research collaboration between the ETSAM - UPM and the PUCP in whose laboratory these tests were carried out.

A comparison of open-source segmentation architectures for dealing with imperfect data from the media in speech synthesis

Traditional Text-To-Speech (TTS) systems have been developed using especially-designed non-expressive scripted recordings. In order to develop a new generation of expressive TTS systems in the Simple4All project, real recordings from the media should be used for training new voices with a whole new range of speaking styles. However, for processing this more spontaneous material, the new systems must be able to deal with imperfect data (multi-speaker recordings, background and foreground music and noise), filtering out low-quality audio segments and creating mono-speaker clusters. In this paper we compare several architectures for combining speaker diarization and music and noise detection which improve the precision and overall quality of the segmentation.