Leveraging the urban soundscape: Auditory perception for smart vehicles
Urban environments are characterised by the presence of distinctive audio signals which alert the drivers to events that require prompt action. The detection and interpretation of these signals would be highly beneficial for smart vehicle systems, as it would provide them with complementary informat...
| Main Authors: | , |
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| Format: | Conference item |
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Institute of Electrical and Electronics Engineers
2017
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| _version_ | 1797095654316572672 |
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| author | Marchegiani, L Posner, H |
| author_facet | Marchegiani, L Posner, H |
| author_sort | Marchegiani, L |
| collection | OXFORD |
| description | Urban environments are characterised by the presence of distinctive audio signals which alert the drivers to events that require prompt action. The detection and interpretation of these signals would be highly beneficial for smart vehicle systems, as it would provide them with complementary information to navigate safely in the environment. In this paper, we present a framework that spots the presence of acoustic events, such as horns and sirens, using a two-stage approach. We first model the urban soundscape and use anomaly detection to identify the presence of an anomalous sound, and later determine the nature of this sound. As the audio samples are affected by copious non-stationary and unstructured noise, which can degrade classification performance, we propose a noise-removal technique to obtain a clean representation of the data we can use for classification and waveform reconstruction. The method is based on the idea of analysing the spectrograms of the incoming signals as images and applying spectrogram segmentation to isolate and extract the alerting signals from the background noise. We evaluate our framework on four hours of urban sounds collected driving around urban Oxford on different kinds of road and in different traffic conditions. When compared to traditional feature representations, such as Mel-frequency cepstrum coefficients, our framework shows an improvement of up to 31% in the classification rate. |
| first_indexed | 2024-03-07T04:30:56Z |
| format | Conference item |
| id | oxford-uuid:ce4a4fb5-2465-415a-bf41-60b54adf1e8a |
| institution | University of Oxford |
| last_indexed | 2024-03-07T04:30:56Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | oxford-uuid:ce4a4fb5-2465-415a-bf41-60b54adf1e8a2022-03-27T07:34:40ZLeveraging the urban soundscape: Auditory perception for smart vehiclesConference itemhttp://purl.org/coar/resource_type/c_5794uuid:ce4a4fb5-2465-415a-bf41-60b54adf1e8aSymplectic Elements at OxfordInstitute of Electrical and Electronics Engineers2017Marchegiani, LPosner, HUrban environments are characterised by the presence of distinctive audio signals which alert the drivers to events that require prompt action. The detection and interpretation of these signals would be highly beneficial for smart vehicle systems, as it would provide them with complementary information to navigate safely in the environment. In this paper, we present a framework that spots the presence of acoustic events, such as horns and sirens, using a two-stage approach. We first model the urban soundscape and use anomaly detection to identify the presence of an anomalous sound, and later determine the nature of this sound. As the audio samples are affected by copious non-stationary and unstructured noise, which can degrade classification performance, we propose a noise-removal technique to obtain a clean representation of the data we can use for classification and waveform reconstruction. The method is based on the idea of analysing the spectrograms of the incoming signals as images and applying spectrogram segmentation to isolate and extract the alerting signals from the background noise. We evaluate our framework on four hours of urban sounds collected driving around urban Oxford on different kinds of road and in different traffic conditions. When compared to traditional feature representations, such as Mel-frequency cepstrum coefficients, our framework shows an improvement of up to 31% in the classification rate. |
| spellingShingle | Marchegiani, L Posner, H Leveraging the urban soundscape: Auditory perception for smart vehicles |
| title | Leveraging the urban soundscape: Auditory perception for smart vehicles |
| title_full | Leveraging the urban soundscape: Auditory perception for smart vehicles |
| title_fullStr | Leveraging the urban soundscape: Auditory perception for smart vehicles |
| title_full_unstemmed | Leveraging the urban soundscape: Auditory perception for smart vehicles |
| title_short | Leveraging the urban soundscape: Auditory perception for smart vehicles |
| title_sort | leveraging the urban soundscape auditory perception for smart vehicles |
| work_keys_str_mv | AT marchegianil leveragingtheurbansoundscapeauditoryperceptionforsmartvehicles AT posnerh leveragingtheurbansoundscapeauditoryperceptionforsmartvehicles |