This study sought to evaluate the effect of speech intensity on performance of the Callsign Acquisition Test (CAT) and Modified Rhyme Test (MRT) presented in noise. Fourteen normally hearing listeners performed both tests in 65 dB A white background noise. Speech intensity varied while background noise remained constant to form speech-to-noise ratios (SNRs) of -18, -15, -12, -9, and -6 dB. Results showed that CAT recognition scores were significantly higher than MRT scores at the same SNRs; however, the scores from both tests were highly correlated and their relationship for the SNRs tested can be expressed by a simple linear function. The concept of CAT can be easily ported to other languages for testing speech communication under adverse listening conditions.
Acoustic parameters were analysed in nine auditoria and multi-purpose conference rooms in the University of Extremadura. Parameters related to the reverberation time, background noise, and intelligibility (both physical measurements of different parameters [Definition (D-50) and STI] and speech tests used to study the subjective response of listeners) were studied. The measurements were compared with some recommendations from the literature and, considering that speech was the main use of the studied rooms, with the intelligibility results. Some different recommendations for reverberation times taken from the literature were analysed. The intelligibility results obtained from the measurements were also compared with the intelligibility results that were determined by the speech tests.
This paper introduces a compelling new way to think about the education and practice of architecture. “Intelligent architecture” is founded on the basis of how the human mind perceives and interacts with the material world. Perhaps surprisingly, this scientifically-conceived process for architectural design and building leads to a more human architecture, one with a renewed respect for traditional systems of architectural design. Scientific insight into architecture’s origins and manner of conception gives us a profound appreciation of useful solutions embedded in our architectural heritage. This development reverses a century-old practice in industrial-modernist architecture, which advocated erasing the past rather than learning from it. By understanding essential human engagement with the built environment, architects are able to foster greater human wellbeing in the material structures they build.
Prof. Edward Nęcka, a cognitive psychologist from the Jagiellonian University and Vice-President of the Polish Academy of Sciences, talks about cognitive misers, memory traps, and confusion in a myriad of new technologies.
This is a modest endeavour written from an engineering perspective by a nonphilosopher to set things straight if somewhat roughly: What does artificial intelligence boil down to? What are its merits and why some dangers may stem from its development in this time of confusion when, to quote Rémi Brague: “From the point of view of technology, man appears as outdated, or at least superfluous”?
This paper presents a deep learning-based image texture recognition system. The methodology taken in this solution is formed in a bottom-up manner. It means we swipe a moving window through the image in order to categorize if a given region belongs to one of the classes seen in the training process. This categorization is done based on the Deep Neural Network (DNN) of fixed architecture. The training process is fully automated regarding the training data preparation, investigation of the best training algorithm, and its hyper-parameters. The only human input to the system is the definition of the categories for further recognition and generation of the samples (region markings) in the external application chosen by the user. The system is tested on road surface images where its task is to categorize image regions to a different road category (e.g. curb, road surface damage, etc.) and is featured with 90% and above accuracy.
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