ConvDTW-ACS: Audio Segmentation for Track Type Detection During Car Manufacturing
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909122583068672 |
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| author | López-Chilet, Álvaro Liu, Zhaoyi Gómez, Jon Ander Alvarez, Carlos Ortiz, Marivi Alonso Mesa, Andres Orejuela Newton, David Wolf-Monheim, Friedrich Michiels, Sam Hughes, Danny |
| author_facet | López-Chilet, Álvaro Liu, Zhaoyi Gómez, Jon Ander Alvarez, Carlos Ortiz, Marivi Alonso Mesa, Andres Orejuela Newton, David Wolf-Monheim, Friedrich Michiels, Sam Hughes, Danny |
| contents | This paper proposes a method for Acoustic Constrained Segmentation (ACS) in audio recordings of vehicles driven through a production test track, delimiting the boundaries of surface types in the track. ACS is a variant of classical acoustic segmentation where the sequence of labels is known, contiguous and invariable, which is especially useful in this work as the test track has a standard configuration of surface types. The proposed ConvDTW-ACS method utilizes a Convolutional Neural Network for classifying overlapping image chunks extracted from the full audio spectrogram. Then, our custom Dynamic Time Warping algorithm aligns the sequence of predicted probabilities to the sequence of surface types in the track, from which timestamps of the surface type boundaries can be extracted. The method was evaluated on a real-world dataset collected from the Ford Manufacturing Plant in Valencia (Spain), achieving a mean error of 166 milliseconds when delimiting, within the audio, the boundaries of the surfaces in the track. The results demonstrate the effectiveness of the proposed method in accurately segmenting different surface types, which could enable the development of more specialized AI systems to improve the quality inspection process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18204 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ConvDTW-ACS: Audio Segmentation for Track Type Detection During Car Manufacturing López-Chilet, Álvaro Liu, Zhaoyi Gómez, Jon Ander Alvarez, Carlos Ortiz, Marivi Alonso Mesa, Andres Orejuela Newton, David Wolf-Monheim, Friedrich Michiels, Sam Hughes, Danny Sound Audio and Speech Processing This paper proposes a method for Acoustic Constrained Segmentation (ACS) in audio recordings of vehicles driven through a production test track, delimiting the boundaries of surface types in the track. ACS is a variant of classical acoustic segmentation where the sequence of labels is known, contiguous and invariable, which is especially useful in this work as the test track has a standard configuration of surface types. The proposed ConvDTW-ACS method utilizes a Convolutional Neural Network for classifying overlapping image chunks extracted from the full audio spectrogram. Then, our custom Dynamic Time Warping algorithm aligns the sequence of predicted probabilities to the sequence of surface types in the track, from which timestamps of the surface type boundaries can be extracted. The method was evaluated on a real-world dataset collected from the Ford Manufacturing Plant in Valencia (Spain), achieving a mean error of 166 milliseconds when delimiting, within the audio, the boundaries of the surfaces in the track. The results demonstrate the effectiveness of the proposed method in accurately segmenting different surface types, which could enable the development of more specialized AI systems to improve the quality inspection process. |
| title | ConvDTW-ACS: Audio Segmentation for Track Type Detection During Car Manufacturing |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2402.18204 |