• The Journal of the Acoustical Society of Korea
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• v.40 no.6
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• pp.615-625
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• 2021

Prior to proposing appropriate standard for subway station platform, this study conducted field measurements to examine characteristics of room acoustics on platform of two subway stations. As a result of analyzing the longitudinal length of the platform, Sound Pressure Level (SPL) decreased (maximum difference : 14 dB), Reverberation Time (RT) tended to increase (maximum difference of 0.8 s ~ 1.5 s), and C50 and D50 were decreased (maximum difference: 5.9 dB ~ 9.1 dB and 31.8 % ~ 37.6 %, respectively) as measurement positions moved away from the sound source. The Interaural Cross-correlation Coefficient (IACC) did not show clear tendency, but it was lower than 0.3 in entire points. It is judged that the subway platform has non-uniform sound field characteristics due to various combinations of direct and reflective sound even though it is finished with a strong reflective material.This indicates that the room acoustic characteristics of the near and far sound field are clearly expressed depending on the source-receiver distances in the subway platform having a long flat shape with a low height compared to the length.Therefore, detailed architectural and electric acoustic design based on the characteristics of each location of speaker and sound receiver in the platform is required for an acoustic design with clear sound information at all positions of the platform.

• The Journal of the Acoustical Society of Korea
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• v.27 no.4
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• pp.163-170
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• 2008

Most of receiving rooms for the measurement of floor impact sound have rectangular shapes with couple of meters of dimension, with reflective finishing, no furniture, no curtains. Modal overlaps in those condition are the major reason for the low reproducibility, and as a matter of course, the low credibility. It is the major purpose of this study that searching for a better measurement method which mitigate the effect of modal overlap on measurement. Two ways of methods are tested. One is the way described in ISO standards which enables controlling the room modes of receiving rooms, the other is the way which enables to get more precise spatial averages in receiving rooms with room modes. It is not easy maintaining the reverberation time of low frequency bands in the range between 1s and 2s, though it is proven to be effective controlling the room modes with base traps. Space-time average SPL's through combinations of rotating microphones are easy to measure, and have good consistencies with average SPL of entire receiving room.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.603-616
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• 2023

In this study, machine learning-based clustering and classification of residential noise in apartment buildings was conducted using frequency and temporal characteristics. First, a residential noise source dataset was constructed . The residential noise source dataset was consisted of floor impact, airborne, plumbing and equipment noise, environmental, and construction noise. The clustering of residential noise was performed by K-Means clustering method. For frequency characteristics, L_eq and L_max values were derived for 1/1 and 1/3 octave band for each sound source. For temporal characteristics, Leq values were derived at every 6 ms through sound pressure level analysis for 5 s. The number of k in K-Means clustering method was determined through the silhouette coefficient and elbow method. The clustering of residential noise source by frequency characteristic resulted in three clusters for both L_eq and L_max analysis. Temporal characteristic clustered residential noise source into 9 clusters for L_eq and 11 clusters for L_max. Clustering by frequency characteristic clustered according to the proportion of low frequency band. Then, to utilize the clustering results, the residential noise source was classified using three kinds of machine learning. The results of the residential noise classification showed the highest accuracy and f1-score for data labeled with L_eq values in 1/3 octave bands, and the highest accuracy and f1-score for classifying residential noise sources with an Artificial Neural Network (ANN) model using both frequency and temporal features, with 93 % accuracy and 92 % f1-score.