Graduate School of Economics and Social Sciences

Permanent URI for this collectionhttps://hdl.handle.net/11693/115676

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Browsing Graduate School of Economics and Social Sciences by Author "Acun, Volkan"

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    An approach based on sound classification to predict soundscape perception through machine learning
    (2021-06) Acun, Volkan
    A growing amount of literature and a series of ISO standards focus on concept, data collection, and data analysis methods of soundscapes. Yet, this field of research still lacks predictive models. We hypothesize that machine learning methods can be used to develop a predictive model by identifying the audio content of soundscapes and correlating it with individuals’ perceived affective response to the soundscapes. Therefore, this research aims to identify machine learning-based sound classification methods for analyzing the audio content of soundscapes and using its output in a second model for evaluating the association between the audio content and perception of the soundscape. We focused on museum soundscapes to conduct our research. The methodology of this thesis is divided into two parts. For the first part, we used Convolutional Neural Networks for classifying the audio content of the soundscape. Due to their limitations, we used a different approach rather than the typical environmental sound classification methods. We used musical instruments for the training dataset and optimized the neural network for this type of task. The convolutional neural network classified the audio content of the soundscapes based on their similarities to the musical instruments of the dataset. We conducted an online soundscape perception survey to measure participants' affective responses to different museum soundscapes for the second part. To predict individuals’ perception of soundscapes, we developed a feedforward neural network model. This model used the audio content output from the sound classification model and the soundscape survey data to predict the perceived affective quality of soundscapes. We concluded the thesis by conducting statistical analyses to explore the association between the variable used in the predictive model.
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    Investigating the effect of indoor soundscaping towards employees’ mood and perception in open plan offices
    (2015-08) Acun, Volkan
    Over the past decade, soundscape studies have proposed ways to differentiate sonic environments and showed that it is not always the sound levels that matters. Meanings associated with sound sources, how they are perceived by the listener and the physical settings are equally important. On the other hand, very few studies are conducted to examine whether these principles of soundscape can be applied to indoor spaces. Aim of this research is to identify sound sources within an open office space, understand how employees’ perceive these sound sources, explore its impact on their mood and task performance. In order to achieve this goal, measurements and interviews are conducted at two different open planed offices. A user focused, subjective, approach of Grounded Theory, is used to capture the lived experience of an open plan office space and examine the effects of indoor soundscape quality towards employees’ perception of their work environment. PANAS (Positive and Negative Affect Schedule) test is conducted to explore employees’ mood. In order to understand the acoustical conditions of case study settings, in-situ measurements of sound levels (Leq), ODEON simulation of Speech Transmission Index (STI) and Reverberation Time (T 30) is used. Semi-structured interviews, as part of Grounded Theory, and PANAS test are conducted with 47 employees. Their responses are used to generate a conceptual framework which conceptualizes employees’ subjective response to the soundscape of their work environment. Generated conceptual framework showed patterns between employees' perception of sound sources, sound preference and type of work they are performing as well as the association between positive affect (PA), negative affect (NA) and soundscape.