Samiotis Ioannis Petros, Qiu Sihang, Lofi Christoph, Yang Jie, Gadiraju Ujwal, Bozzon Alessandro
Department of Software Technology, Delft University of Technology, Delft, Netherlands.
Hunan Institute of Advanced Technology, Changsha, China.
Front Artif Intell. 2022 Jun 14;5:828733. doi: 10.3389/frai.2022.828733. eCollection 2022.
Music content annotation campaigns are common on paid crowdsourcing platforms. Crowd workers are expected to annotate complex music artifacts, a task often demanding specialized skills and expertise, thus selecting the right participants is crucial for campaign success. However, there is a general lack of deeper understanding of the distribution of musical skills, and especially auditory perception skills, in the worker population. To address this knowledge gap, we conducted a user study ( = 200) on Prolific and Amazon Mechanical Turk. We asked crowd workers to indicate their musical sophistication through a questionnaire and assessed their music perception skills through an audio-based skill test. The goal of this work is to better understand the extent to which crowd workers possess higher perceptions skills, beyond their own musical education level and self reported abilities. Our study shows that untrained crowd workers can possess high perception skills on the music elements of , and ; skills that can be useful in a plethora of annotation tasks in the music domain.
音乐内容标注活动在付费众包平台上很常见。众包工作者需要对复杂的音乐制品进行标注,这一任务通常需要专业技能和专业知识,因此选择合适的参与者对于活动的成功至关重要。然而,人们普遍缺乏对众包工作者群体中音乐技能,尤其是听觉感知技能分布的深入了解。为了填补这一知识空白,我们在Prolific和亚马逊土耳其机器人平台上进行了一项用户研究(n = 200)。我们要求众包工作者通过问卷表明他们的音乐素养,并通过基于音频的技能测试评估他们的音乐感知技能。这项工作的目标是更好地了解众包工作者在超出其自身音乐教育水平和自我报告能力的情况下,拥有较高感知技能的程度。我们的研究表明,未经训练的众包工作者在音高、节奏和音色的音乐元素方面可以拥有较高的感知技能;这些技能在音乐领域的大量标注任务中可能会很有用。