Prachee Priyadarshinee

In this study, we develop a noise annoyance prediction tool using deep learning in Singapore, a densely populated city-state where a significant portion of the population resides in public housing near various noise sources like MRT, highways, bus routes, and construction sites. To investigate short-term annoyance caused by surrounding noises, we created an easily accessible web-based subjective listening test. We created a noise dataset featuring typical Singaporean noise sources, including traffic (buses, highways), trains, aviation, neighbourhood activities (playgrounds, schools, hawker centers, funerals, wildlife, home renovations), and construction. Participants were exposed to 35 noise stimuli, lasting between 15 to 30 seconds, and asked to rate perceived annoyance on a 5-point scale, with 5 being the most annoying. Using these reported annoyance levels, we ranked the stimuli relative to each other and categorized them into three classes: low, medium, and high noise annoyance. Using these three labels, Long-Short Term Memory (LSTM) networks were trained to predict the perceived annoyance of new audio samples. Such a perceived annoyance assessment tool for new audio samples can help in addressing specific noise concerns of residents, leading to more effective and targeted noise control strategies, and consequently creating a quieter and more pleasant urban living environment.

Acoustic simulation tools, although common in the acoustic evaluation of buildings with large atrium space, are costly both in terms of time and resources. This study aims to develop an efficient prediction tool for Reverberation Time (RT) and Sound Pressure Level (SPL) within the atriums using deep learning, with Building Information Modelling (BIM) files as the only input. Initially, a 3‐D acoustic simulation model was benchmarked against experimental measurements of RT for an existing building’s atrium, demonstrating an agreement within 15%. Subsequently, BIM files of 60 buildings were used to simulate RT and SPL at various listener locations within their atrium spaces. The BIM files provided essential geometry information, including atrium shape, dimensions, door placements, floor plans, material properties, and sound sources. This dataset was then utilized to train deep learning algorithms, enabling rapid and convenient predictions of RT and SPL for any new building's atrium space. The proposed prediction model serves as a valuable tool for architectural planning and noise regulation during the early design stages. By relying solely on basic building information from the BIM file, this tool obviates the need for time-consuming and computationally expensive simulation software typically used for acoustic evaluations in large atrium spaces.

Automated techniques to detect Alzheimer’s Dementia through the use of audio recordings of spontaneous speech are now available with varying degrees of reliability. Here, we present a systematic comparison across different modalities, granularities and machine learning models to guide in choosing the most effective tools. Specifically, we present a multi-modal approach (audio and text) for the automatic detection of Alzheimer’s Dementia from recordings of spontaneous speech. Sixteen features, including four feature extraction methods (Energy–Time plots, Keg of Text Analytics, Keg of Text Analytics-Extended and Speech to Silence ratio) not previously applied in this context were tested to determine their relative performance. These features encompass two modalities (audio vs. text) at two resolution scales (frame-level vs. file-level). We compared the accuracy resulting from these features and found that text-based classification outperformed audio-based classification with the best performance attaining 88.7%, surpassing other reports to-date relying on the same dataset. For text-based classification in particular, the best file-level feature performed 9.8% better than the frame-level feature. However, when comparing audio-based classification, the best frame-level feature performed 1.4% better than the best file-level feature. This multi-modal multi-model comparison at high- and low-resolution offers insights into which approach is most efficacious, depending on the sampling context. Such a comparison of the accuracy of Alzheimer’s Dementia classification using both frame-level and file-level granularities on audio and text modalities of different machine learning models on the same dataset has not been previously addressed. We also demonstrate that the subject’s speech captured in short time frames and their dynamics may contain enough inherent information to indicate the presence of dementia. Overall, such a systematic analysis facilitates the identification of Alzheimer’s Dementia quickly and non-invasively, potentially leading to more timely interventions and improved patient outcomes.