• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.565-571
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• 2023

With the introduction of the post-verification system, the measurement of floor impact noise performance on-site has become mandatory, and the evaluation method has changed. To track the performance changes since the policy implementation, research is needed on how the characteristics of heavyweight impact sound change according to the varied evaluation method. In this study, we analyzed the contribution rate of the frequency band-specific sound pressure level on the single-number quantity for a multi-family housing unit with the same floor plan and floor structure, comprising 59 households, based on the changed impact sources and evaluation indicators. It is difficult to compare simply because the method of calculating contributions by frequency band according to the single-day evaluation is different, but the average contribution rate of 63 Hz was 80.8 % in the evaluation method before the introduction of the post-confirmation system (Tire measurement and evaluated as L'_i,Fmax,AW), and the average contribution rate of 125 Hz was 19.2 %. The current evaluation method (rubber ball measurement and evaluation as L'_iA,Fmax) shows that the contribution rate has decreased to 33.1 % on average at 50 Hz ~ 80 Hz, 58.7 % on average at 100 Hz ~ 160 Hz, 6.9 % on average at 200 Hz ~ 315 Hz, and 1.3 % on average at 400 Hz ~ 630 Hz. This result is a case analysis for the target apartment house, and it is necessary to analyze measurement data for more diverse apartment houses.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.9-17
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• 2022

As the COVID-19 pandemic situation worsens, the time spent indoors increases, and the exposure to indoor environmental pollution such as indoor air pollution and noise also increases, causing problems such as deterioration of human health, stress, and discord between neighbors. This paper designs and implements a system that measures and monitors indoor air quality and noise, which are representative evaluation criteria of the indoor environment. The system proposed in this paper consists of a particulate matter measurement subsystem that measures and corrects the concentration of particulate matters to monitor indoor air quality, and a noise measurement subsystem that detects changes in sound and converts it to a sound pressure level. The concentration of indoor particulate matters is measured using a laser-based light scattering method, and an error caused by temperature and humidity is compensated in this paper. For indoor noise measurement, the voltage measured through a microphone is basically measured, Fourier transform is performed to classify it by frequency, and then A-weighting is performed to correct loudness equality. Then, the RMS value is obtained, high-frequency noise is removed by performing time-weighting, and then SPL is obtained. Finally, the equivalent noise level for 1 minute and 5 minutes are calculated to show the indoor noise level. In order to classify noise into direct impact sound and air transmission noise, a piezo vibration sensors is mounted to determine the presence or absence of direct impact transmitted through the wall. For performance evaluation, the error of particulate matter measurement is analyzed through TSI's AM510 instrument. and compare the noise error with CEM's noise measurement system.

• 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.