论文标题
听到她的恐惧:在印度对犯罪的犯罪敏感的数据索赔
Hear Her Fear: Data Sonification for Sensitizing Society on Crime Against Women in India
论文作者
论文摘要
数据超索是通过声音表示数据的一种手段,并已在各种应用中使用。在印度,针对妇女的犯罪一直是一个不断提高的关注点。我们探索数据成功的潜力,以与印度各州针对妇女犯罪的敏感数据进行沉浸式参与。从国家记录中获取了九十五个印度州的九个犯罪类别的数据。采用了参数映射和听觉图标的超拟合作技术:纳入频率,振幅和音音等声音参数以表示犯罪数据,而女性尖叫声则用作听觉图标来强调创伤经历。较高的犯罪率分配了更高的频率,更严厉的尖叫纹理和较大的幅度。开发了一个用户友好的界面,具有多个用于顺序和比较数据超声的选项。通过界面,用户可以评估和比较不同州,年或犯罪类别中针对妇女的犯罪程度。声音空间化用于使听众沉浸在声音中,并进一步增强超音速体验。为了评估和验证有效性,对通过问卷获得的反馈进行了对二十名参与者的用户研究。响应表明,参与者可以轻松理解数据中的趋势,并发现数据超索经验的影响力。因此,SONIFICATY可能被证明是与社会和人类研究有关的领域中数据表示的宝贵工具。
Data sonification is a means of representing data through sound and has been utilized in a variety of applications. Crime against women has been a rising concern in India. We explore the potential of data sonification to provide an immersive engagement with sensitive data on crime against women in Indian states. The data for nine crime categories covering thirty-five Indian states over a period of twelve years is acquired from National records. Sonification techniques of parameter mapping and auditory icons are adopted: sound parameters such as frequencies, amplitudes and timbres are incorporated to represent the crime data, and audio sounds of women screams are employed as auditory icons to emphasize the traumatic experience. Higher crime rates are assigned higher frequencies, harsher scream textures and larger amplitudes. A user-friendly interface is developed with multiple options for sequential and comparative data sonification. Through the interface, a user can evaluate and compare the extent of crime against women in different states, years or crime categories. Sound spatialization is used to immerse the listener in the sound and further intensify the sonification experience. To assess and validate effectiveness, a user study on twenty participants is conducted with feedback obtained through questionnaires. The responses indicate that the participants could comprehend trends in the data easily and found the data sonification experience impactful. Sonification may therefore prove to be a valuable tool for data representation in fields related to social and human studies.