Spectrogram Window Comparison: Cough Sound Recognition using Convolutional Neural Network

 Cough is one of the most common symptoms of diseases, especially respiratory diseases. Quick cough detection can be the key to the current pandemic of COVID-19. Good cough recognition is the one that uses non-intrusive tools such as a mobile phone microphone that does not disable human activities like stick sensors. To do sound-only detection, Deep Learning current best method Convolutional Neural Network (CNN) is used. However, CNN needs image input while sound input differs (one dimension rather than two). An extra process is needed, converting sound data to image data using a spectrogram. When building a spectrogram, there is a question about the best size. This research will compare the spectrogram's size, called Spectrogram Window, by the performance. The result is that windows with 4 seconds have the highest F1-score performance at 92.9%. Therefore, a window of around 4 seconds will perform better for sound recognition problems

Improving the Teaching of Pronunciation in A Junior High School by Integrating Segmental and Suprasegmental Learning

This research aimed to improve pronunciation teaching for junior high school students by integrating the learning material's segmental and suprasegmental features of pronunciation. This classroom action research was conducted in three cycles. Each cycle consists of planning, action, observation, and reflection. This study implemented six actions, i.e., 1) the use of classroom English, 2) implementing the integration of pronunciation teaching); 3) giving feedback on students’ pronunciation; 4) introducing phonetic transcription; 5) implementing engaging activities to teach pronunciation; and 6) reinforcing pronunciation. The data were collected through interviews, class observation, and pronunciation assessment using a rubric developed based on the indicators of hearing sounds, stress and intonation, and imitation. The qualitative data from the field notes and interviews were analyzed qualitatively. Meanwhile, the quantitative data from the assessment results of student pronunciation were analyzed using the paired-t test to confirm the significant difference before and after the actions. The research results show that integrating segmental and suprasegmental learning improved pronunciation teaching for Junior High School students (as reflected in the data from the interview and field notes), and their pronunciation assessment results improved student pronunciation. In all cycles, integrating segmental and suprasegmental learning in pronunciation teaching improved student pronunciation