ConvDTW-ACS: Audio Segmentation for Track Type Detection During Car Manufacturing

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, contigu...

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Bibliographic Details
Published in:arXiv.org 2024-02
Main Authors: López-Chilet, Álvaro, Liu, Zhaoyi, Jon Ander Gómez, Alvarez, Carlos, Marivi Alonso Ortiz, Andres Orejuela Mesa, Newton, David, Wolf-Monheim, Friedrich, Michiels, Sam, Hughes, Danny
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Boundaries
Image classification
Manufacturing
Test tracks
Online Access:Get full text
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Summary: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.
ISSN:2331-8422