UN15: An Urban Noise Dataset Coupled with Time–Frequency Attention for Environmental Sound Classification
With the increasing severity of urban noise pollution, its detrimental impact on public health has garnered growing attention. However, accurate identification and classification of noise sources in complex urban acoustic environments remain major technical challenges for achieving refined noise man...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/15/8413 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849770919515389952 |
|---|---|
| author | Yu Shen Ge Cao Huan-Yu Dong Bo Dong Chang-Myung Lee |
| author_facet | Yu Shen Ge Cao Huan-Yu Dong Bo Dong Chang-Myung Lee |
| author_sort | Yu Shen |
| collection | DOAJ |
| description | With the increasing severity of urban noise pollution, its detrimental impact on public health has garnered growing attention. However, accurate identification and classification of noise sources in complex urban acoustic environments remain major technical challenges for achieving refined noise management. To address this issue, this study presents two key contributions. First, we construct a new urban noise classification dataset, namely the urban noise 15-category dataset (UN15), which consists of 1620 audio clips from 15 representative categories, including traffic, construction, crowd activity, and commercial noise, recorded from diverse real-world urban scenes. Second, we propose a novel deep neural network architecture based on a residual network and integrated with a time–frequency attention mechanism, referred to as residual network with temporal–frequency attention (ResNet-TF). Extensive experiments conducted on the UN15 dataset demonstrate that ResNet-TF outperforms several mainstream baseline models in both classification accuracy and robustness. These results not only verify the effectiveness of the proposed attention mechanism but also establish the UN15 dataset as a valuable benchmark for future research in urban noise classification. |
| format | Article |
| id | doaj-art-d2ce23f4e79a496fae9b6ded64b7a08d |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-d2ce23f4e79a496fae9b6ded64b7a08d2025-08-20T03:02:48ZengMDPI AGApplied Sciences2076-34172025-07-011515841310.3390/app15158413UN15: An Urban Noise Dataset Coupled with Time–Frequency Attention for Environmental Sound ClassificationYu Shen0Ge Cao1Huan-Yu Dong2Bo Dong3Chang-Myung Lee4Department of Mechanical and Automotive Engineering, University of Ulsan, Ulsan 44610, Republic of KoreaDepartment of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of KoreaSchool of Automotive Engineering, Liaoning University of Technology, Jinzhou 121001, ChinaDepartment of Mechanical and Automotive Engineering, University of Ulsan, Ulsan 44610, Republic of KoreaDepartment of Mechanical and Automotive Engineering, University of Ulsan, Ulsan 44610, Republic of KoreaWith the increasing severity of urban noise pollution, its detrimental impact on public health has garnered growing attention. However, accurate identification and classification of noise sources in complex urban acoustic environments remain major technical challenges for achieving refined noise management. To address this issue, this study presents two key contributions. First, we construct a new urban noise classification dataset, namely the urban noise 15-category dataset (UN15), which consists of 1620 audio clips from 15 representative categories, including traffic, construction, crowd activity, and commercial noise, recorded from diverse real-world urban scenes. Second, we propose a novel deep neural network architecture based on a residual network and integrated with a time–frequency attention mechanism, referred to as residual network with temporal–frequency attention (ResNet-TF). Extensive experiments conducted on the UN15 dataset demonstrate that ResNet-TF outperforms several mainstream baseline models in both classification accuracy and robustness. These results not only verify the effectiveness of the proposed attention mechanism but also establish the UN15 dataset as a valuable benchmark for future research in urban noise classification.https://www.mdpi.com/2076-3417/15/15/8413environmental sound classificationUN15 datasetconvolutional neural networkattention mechanism |
| spellingShingle | Yu Shen Ge Cao Huan-Yu Dong Bo Dong Chang-Myung Lee UN15: An Urban Noise Dataset Coupled with Time–Frequency Attention for Environmental Sound Classification Applied Sciences environmental sound classification UN15 dataset convolutional neural network attention mechanism |
| title | UN15: An Urban Noise Dataset Coupled with Time–Frequency Attention for Environmental Sound Classification |
| title_full | UN15: An Urban Noise Dataset Coupled with Time–Frequency Attention for Environmental Sound Classification |
| title_fullStr | UN15: An Urban Noise Dataset Coupled with Time–Frequency Attention for Environmental Sound Classification |
| title_full_unstemmed | UN15: An Urban Noise Dataset Coupled with Time–Frequency Attention for Environmental Sound Classification |
| title_short | UN15: An Urban Noise Dataset Coupled with Time–Frequency Attention for Environmental Sound Classification |
| title_sort | un15 an urban noise dataset coupled with time frequency attention for environmental sound classification |
| topic | environmental sound classification UN15 dataset convolutional neural network attention mechanism |
| url | https://www.mdpi.com/2076-3417/15/15/8413 |
| work_keys_str_mv | AT yushen un15anurbannoisedatasetcoupledwithtimefrequencyattentionforenvironmentalsoundclassification AT gecao un15anurbannoisedatasetcoupledwithtimefrequencyattentionforenvironmentalsoundclassification AT huanyudong un15anurbannoisedatasetcoupledwithtimefrequencyattentionforenvironmentalsoundclassification AT bodong un15anurbannoisedatasetcoupledwithtimefrequencyattentionforenvironmentalsoundclassification AT changmyunglee un15anurbannoisedatasetcoupledwithtimefrequencyattentionforenvironmentalsoundclassification |