Evaluation of Two Alternative Procedures for Measuring Airflow Resistance of Sound Absorbing Materials

It is well known that sound absorption and sound transmission properties of open porous materials are highly dependent on their airflow resistance values. Low values of airflow resistance indicate little resistance for air streaming through the porous material and high values are a sign that most of the pores inside the material are closed. The laboratory procedures for measuring airflow resistance have been stan- dardized by several organizations, including ISO and ASTM for both alternate flow and continuous flow. However, practical implementation of these standardized methods could be both complex and expensive. In this work, two indirect alternative measurement procedures were compared against the alternate flow standardized technique. The techniques were tested using three families of eco-friendly sound absorbent materials: recycled polyurethane foams, coconut natural fibres, and recycled polyester fibres. It is found that the values of airflow resistance measured using both alternative methods are very similar. There is also a good correlation between the values obtained through alternative and standardized methods.

Detecting implicit expressions of affect in text using EmotiNet and its extensions

In the past years, an important volume of research in Natural Language Processing has concentrated on the development of automatic systems to deal with affect in text. The different approaches considered dealt mostly with explicit expressions of emotion, at word level. Nevertheless, expressions of emotion are often implicit, inferrable from situations that have an affective meaning. Dealing with this phenomenon requires automatic systems to have “knowledge” on the situation, and the concepts it describes and their interaction, to be able to “judge” it, in the same manner as a person would. This necessity motivated us to develop the EmotiNet knowledge base — a resource for the detection of emotion from text based on commonsense knowledge on concepts, their interaction and their affective consequence. In this article, we briefly present the process undergone to build EmotiNet and subsequently propose methods to extend the knowledge it contains. We further on analyse the performance of implicit affect detection using this resource. We compare the results obtained with EmotiNet to the use of alternative methods for affect detection. Following the evaluations, we conclude that the structure and content of EmotiNet are appropriate to address the automatic treatment of implicitly expressed affect, that the knowledge it contains can be easily extended and that overall, methods employing EmotiNet obtain better results than traditional emotion detection approaches.