• Journal of the Korean Wood Science and Technology
• /
• v.47 no.4
• /
• pp.425-441
• /
• 2019

In this study, sound absorption capability and sound transmission loss of several kinds of target densities and thickness for six species of wood bark particle were estimated by the transfer function and transfer matrix methods. Resultantly, the mean sound absorption coefficient of a 100-mm thick Hinoki wood bark particle mat was 0.90 in the frequency range of 100-6400 Hz, whereas the mean sound absorption rate of a 50-mm thick Hinoki wood bark particle mat was 0.84 in the same frequency range. Particularly, at a thickness of 100 mm, it reached almost up to 100% in the frequency range of 1 KHz. The sound transmission losses of 100-mm thick Hinoki wood bark particle mat with a target density of 0.16 at 500 and 1000 Hz were 15.30 and 15.73 dB, respectively. When a 10-mm thick plywood was attached to the back of the wood particle mat, the sound transmission losses was increased by 20-30 dB. Wood bark can be used as an acoustical material owing to its high sound absorption rate and transmission loss.

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