Bones Oliver, Cox Trevor J, Davies William J
Acoustics Research Centre, University of Salford, Salford, United Kingdom.
Front Psychol. 2018 Jul 30;9:1277. doi: 10.3389/fpsyg.2018.01277. eCollection 2018.
Five evidence-based taxonomies of everyday sounds frequently reported in the soundscape literature have been generated. An online sorting and category-labeling method that elicits rather than prescribes descriptive words was used. A total of = 242 participants took part. The main categories of the soundscape taxonomy were people, nature, and manmade, with each dividing into further categories. Sounds within the nature and manmade categories, and two further individual sound sources, dogs, and engines, were explored further by repeating the procedure using multiple exemplars. By generating multidimensional spaces containing both sounds and the spontaneously generated descriptive words the procedure allows for the interpretation of the psychological dimensions along which sounds are organized. This reveals how category formation is based upon different cues - sound source-event identification, subjective-states, and explicit assessment of the acoustic signal - in different contexts. At higher levels of the taxonomy the majority of words described sound source-events. In contrast, when categorizing dog sounds a greater proportion of the words described subjective-states, and valence and arousal scores of these words correlated with their coordinates along the first two dimensions of the data. This is consistent with valence and arousal judgments being the primary categorization strategy used for dog sounds. In contrast, when categorizing engine sounds a greater proportion of the words explicitly described the acoustic signal. The coordinates of sounds along the first two dimensions were found to correlate with fluctuation strength and sharpness, consistent with explicit assessment of acoustic signal features underlying category formation for engine sounds. By eliciting descriptive words the method makes explicit the subjective meaning of these judgments based upon valence and arousal and acoustic properties, and the results demonstrate distinct strategies being spontaneously used to categorize different types of sounds.
声音景观文献中经常报道的五种基于证据的日常声音分类法已经生成。使用了一种在线分类和类别标注方法，该方法引出而非规定描述性词语。共有242名参与者参与。声音景观分类法的主要类别是人类、自然和人造，每个类别又进一步细分。通过使用多个示例重复该程序，对自然和人造类别中的声音以及另外两个单独的声源——狗叫声和发动机声进行了进一步探索。通过生成包含声音和自发产生的描述性词语的多维空间，该程序允许解释声音组织所依据的心理维度。这揭示了类别形成是如何在不同情境下基于不同线索——声源 - 事件识别、主观状态以及对声学信号的明确评估——的。在分类法的较高层次上，大多数词语描述声源 - 事件。相比之下，在对狗叫声进行分类时，更大比例的词语描述主观状态，并且这些词语的效价和唤醒分数与它们在数据的前两个维度上的坐标相关。这与效价和唤醒判断是用于狗叫声的主要分类策略一致。相反，在对发动机声音进行分类时，更大比例的词语明确描述声学信号。发现声音在前两个维度上的坐标与波动强度和清晰度相关，这与对发动机声音类别形成背后的声学信号特征的明确评估一致。通过引出描述性词语，该方法明确了基于效价、唤醒和声学特性的这些判断的主观意义，并且结果表明自发使用了不同的策略来对不同类型的声音进行分类。
