Distinctive Features and Digital Filtration System (DFS)

Abstract

This study aimed to evaluate the potential of DFS to help identify distinctive sound features easily and quickly. Through 20 participants in a between-group format, ten of whom were placed into each group, the researcher wanted to answer the main question of the study: How effective is using DFS to identify distinctive sound features? The participants in the study were tasked with identifying sounds through their distinctive features using either the paper-assisted matrix or the Excel filter matrix. The results show how the Excel filtering cohort outperformed the paper-based matrix one in accuracy and speediness. Group one using the Excel filtration performed perfectly with 100% accuracy in comparison to group two, which used a matrix on a piece of paper and gave accuracy that fluctuated between 40% and 80%. The group employing Excel filtration had better response time, with speed scores ranging between 0.4 min and 0.15 min compared with the paper-based matrix that demonstrated speed scores ranging from 1 to 3 min. The results showed that significant differences existed among the medians of accuracy (p < 0.05) and speed (p < 0.05) between the two studied groups. It thus proved to be a better approach because of its increased precision and faster reactive speed compared to the paper-based matrix, which was used for this test.