Investigation of perceived annoyance in indoor workplaces using short and ultra-short sound samples

Typically, metrics crucial for assessing sound quality are derived from high-quality audio samples obtained in controlled environments. However, due to the sensitive nature of workplace settings, recording audio samples on-site becomes impractical. Privacy emerges as a significant concern for all when collecting audio samples. Consequently, best practice would require that all analyses must occur either locally on a device used to record audio or in the cloud, utilizing a purely mathematical approach. The paper aims to formulate methodologies for determining psycho-acoustic sound quality metrics without the need to analyze audio data directly. This can be achieved by collecting and analyzing data in small batches. The variation of these metrics over these smaller samples can serve as an indicator of sound quality. Initiating the determination of values for psycho-acoustic sound quality metrics such as loudness (N), sharpness (S), roughness (R) and fluctuation strength (FS) in short and ultra-short batches, followed by monitoring their variation over an extended period (e.g., 5 minutes), provides additional insight into the sound characteristics of the space. Using the obtained psycho-acoustic sound quality metrics, we will explore the perceived annoyance among occupants through indoor soundscape assessments.