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.

翻译：本文提出了一种声学约束分割（ACS）方法，用于对在生产测试轨道上行驶的车辆音频进行分割，以界定轨道中路面类型的边界。ACS是经典声学分割的一个变体，其中标签序列是已知的、连续且不变的，这在本研究中特别有用，因为测试轨道具有标准的路面类型配置。所提出的ConvDTW-ACS方法利用卷积神经网络对从完整音频频谱图中提取的重叠图像块进行分类。随后，我们定制的动态时间规整算法将预测概率序列与轨道中的路面类型序列进行对齐，从而可以提取出路面类型边界的时间戳。该方法在西班牙瓦伦西亚福特制造工厂收集的真实数据集上进行了评估，在音频中界定轨道路面边界时实现了平均166毫秒的误差。结果表明，所提出的方法能够有效且准确地分割不同的路面类型，这有助于开发更专业的人工智能系统来改进质量检测流程。