• The Journal of the Acoustical Society of Korea
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• v.28 no.3
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• pp.260-267
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• 2009

It is very difficult to form a desired complex sound field at a designated region precisely as an application of acoustic arrays, which is one of important objects of array systems. To solve the problem, a filter design method was suggested, which employed the concept of an inverse method using the acoustical holography based on the boundary element method. In the acoustical holography used for the source identification, the measured field data are employed to reconstruct the vibro-acoustic parameters on the source surface. In the analogous problem of source array design, the desired field data at some specific points in the sound field was set as constraints and the volume velocity at the surface points of the source plane became the source signal to satisfy the desired sound field. In the filter design, the constraints for the desired sound field are set, first. The array source and given space are modelled by the boundary elements. Then, the desired source parameters are inversely calculated in a way similar to the holographic source identification method. As a test example, a target field comprised of a quiet region and a plane wave propagation region was simultaneously realized by using the array with 16 loudspeakers.

• 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.