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Spectral features for audio based vehicle and engine classification

机译：基于音频的车辆和发动机分类的频谱特征

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摘要

In this paper we address automatic vehicle and engine identification based on audio information. Such data depend on many factors, including vehicle type, tires, speed and its change, as well as road type. In our previous research we designed a feature set for selected vehicle classes, discriminating pairs of classes. Later, we decided to expand the feature vector and find the best feature set (mainly based on spectral descriptors), possibly representative for each investigated vehicle category, which can be applied to a bigger data set, with more classes. The experiments were performed first on on-road recordings, and then continued with test bench (dyno) recordings. The paper also shows problems related to vehicles classification, which is detailed in official documents by national authority for issues related to the national road system, but simplified for automatic identification purposes. Experiments on audio-based vehicle type and engine type identification are presented and conclusions are shown.
机译：在本文中，我们基于音频信息解决了自动车辆和发动机识别的问题。此类数据取决于许多因素，包括车辆类型，轮胎，速度及其变化以及道路类型。在我们以前的研究中，我们为选定的车辆类别设计了一个功能集，以区分成对的类别。后来，我们决定扩展特征向量并找到最佳特征集（主要基于频谱描述符），该特征集可能代表每种研究的车辆类别，可以应用于更多类别的更大数据集。实验首先在道路记录中进行，然后继续进行测试台（dyno）记录。该文件还显示了与车辆分类有关的问题，国家当局在官方文件中针对与国家道路系统有关的问题进行了详细说明，但出于自动识别的目的而进行了简化。提出了基于音频的车辆类型和发动机类型识别的实验，并给出了结论。

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