Sound event detection by intermittency ratio criterium and source classification by deep learning techniques
Urban environments are characterized by a complex interplay of various sound sources, which significantly influence the overall soundscape quality. This study presents a methodology that combines the intermittency ratio (IR) metric for acoustic event detection with deep learning (DL) techniques for...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-04-01
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| Series: | Noise Mapping |
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| Online Access: | https://doi.org/10.1515/noise-2024-0014 |
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| _version_ | 1850148773399887872 |
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| author | Vidaña-Vila Ester Brambilla Giovanni Alsina-Pagès Rosa Ma |
| author_facet | Vidaña-Vila Ester Brambilla Giovanni Alsina-Pagès Rosa Ma |
| author_sort | Vidaña-Vila Ester |
| collection | DOAJ |
| description | Urban environments are characterized by a complex interplay of various sound sources, which significantly influence the overall soundscape quality. This study presents a methodology that combines the intermittency ratio (IR) metric for acoustic event detection with deep learning (DL) techniques for the classification of sound sources associated with these events. The aim is to provide an automated tool for detecting and categorizing polyphonic acoustic events, thereby enhancing our ability to assess and manage environmental noise. Using a dataset collected in the city center of Barcelona, our results demonstrate the effectiveness of the IR metric in successfully detecting events from diverse categories. Specifically, the IR captures the temporal variations of sound pressure levels due to significant noise events, enabling their detection but not providing information on the associated sound sources. To fill this weakness, the DL-based classification system, which uses a MobileNet convolutional neural network, shows promise in identifying foreground sound sources. Our findings highlight the potential of DL techniques to automate the classification of sound sources, providing valuable insights into the acoustic environment. The proposed methodology of combining the two above techniques represents a step forward in automating acoustic event detection and classification in urban soundscapes and providing important information to manage noise mitigation actions. |
| format | Article |
| id | doaj-art-204657eae6be40b29899dfd8f3a134e8 |
| institution | OA Journals |
| issn | 2084-879X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Noise Mapping |
| spelling | doaj-art-204657eae6be40b29899dfd8f3a134e82025-08-20T02:27:09ZengDe GruyterNoise Mapping2084-879X2025-04-011211011410.1515/noise-2024-0014Sound event detection by intermittency ratio criterium and source classification by deep learning techniquesVidaña-Vila Ester0Brambilla Giovanni1Alsina-Pagès Rosa Ma2Human-Environment Research (HER), La Salle, Universitat Ramon Llull – c/Sant Joan de la Salle, 42, 08022Barcelona, Catalonia, SpainDepartment of Earth and Environmental Sciences (DISAT), University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126, Milan, ItalyHuman-Environment Research (HER), La Salle, Universitat Ramon Llull – c/Sant Joan de la Salle, 42, 08022Barcelona, Catalonia, SpainUrban environments are characterized by a complex interplay of various sound sources, which significantly influence the overall soundscape quality. This study presents a methodology that combines the intermittency ratio (IR) metric for acoustic event detection with deep learning (DL) techniques for the classification of sound sources associated with these events. The aim is to provide an automated tool for detecting and categorizing polyphonic acoustic events, thereby enhancing our ability to assess and manage environmental noise. Using a dataset collected in the city center of Barcelona, our results demonstrate the effectiveness of the IR metric in successfully detecting events from diverse categories. Specifically, the IR captures the temporal variations of sound pressure levels due to significant noise events, enabling their detection but not providing information on the associated sound sources. To fill this weakness, the DL-based classification system, which uses a MobileNet convolutional neural network, shows promise in identifying foreground sound sources. Our findings highlight the potential of DL techniques to automate the classification of sound sources, providing valuable insights into the acoustic environment. The proposed methodology of combining the two above techniques represents a step forward in automating acoustic event detection and classification in urban soundscapes and providing important information to manage noise mitigation actions.https://doi.org/10.1515/noise-2024-0014intermittency ratiosound event detectionconvolutional neural networksound source identificationurban noise |
| spellingShingle | Vidaña-Vila Ester Brambilla Giovanni Alsina-Pagès Rosa Ma Sound event detection by intermittency ratio criterium and source classification by deep learning techniques Noise Mapping intermittency ratio sound event detection convolutional neural network sound source identification urban noise |
| title | Sound event detection by intermittency ratio criterium and source classification by deep learning techniques |
| title_full | Sound event detection by intermittency ratio criterium and source classification by deep learning techniques |
| title_fullStr | Sound event detection by intermittency ratio criterium and source classification by deep learning techniques |
| title_full_unstemmed | Sound event detection by intermittency ratio criterium and source classification by deep learning techniques |
| title_short | Sound event detection by intermittency ratio criterium and source classification by deep learning techniques |
| title_sort | sound event detection by intermittency ratio criterium and source classification by deep learning techniques |
| topic | intermittency ratio sound event detection convolutional neural network sound source identification urban noise |
| url | https://doi.org/10.1515/noise-2024-0014 |
| work_keys_str_mv | AT vidanavilaester soundeventdetectionbyintermittencyratiocriteriumandsourceclassificationbydeeplearningtechniques AT brambillagiovanni soundeventdetectionbyintermittencyratiocriteriumandsourceclassificationbydeeplearningtechniques AT alsinapagesrosama soundeventdetectionbyintermittencyratiocriteriumandsourceclassificationbydeeplearningtechniques |