Hear Her Fear: Data Sonification for Sensitizing Society on Crime Against Women in India
HHear Her Fear : Data Sonification for Sensitizing Society onCrime Against Women in India
Surabhi S. Nath
Computer Science and EngineeringIIIT DelhiNew Delhi, [email protected]
ABSTRACT
Data sonification is a means of representing data through sound andhas been utilized in a variety of applications. Crime against womenhas been a rising concern in India. We explore the potential of datasonification to provide an immersive engagement with sensitivedata on crime against women in Indian states. The data for ninecrime categories covering thirty-five Indian states over a periodof twelve years is acquired from National records. Sonificationtechniques of parameter mapping and auditory icons are adopted:sound parameters such as frequencies, amplitudes and timbresare incorporated to represent the crime data, and audio soundsof women screams are employed as auditory icons to emphasizethe traumatic experience. Higher crime rates are assigned higherfrequencies, harsher scream textures and larger amplitudes. A user-friendly interface is developed with multiple options for sequentialand comparative data sonification. Through the interface, a usercan evaluate and compare the extent of crime against women indifferent states, years or crime categories. Sound spatialization isused to immerse the listener in the sound and further intensify thesonification experience. To assess and validate effectiveness, a userstudy on twenty participants is conducted with feedback obtainedthrough questionnaires. The responses indicate that the participantscould comprehend trends in the data easily and found the datasonification experience impactful. Sonification may therefore proveto be a valuable tool for data representation in fields related tosocial and human studies.
CCS CONCEPTS • Human-centered computing → User interface toolkits ; Ac-cessibility systems and tools . KEYWORDS
Data sonification, Parameter mapping, Auditory icons, Sound spa-tialization, Crime against women, User interface, User study
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IndiaHCI 2020, November 5–8, 2020, Online, India © 2020 Association for Computing Machinery.ACM ISBN 978-1-4503-8944-0/20/11...$15.00https://doi.org/10.1145/3429290.3429307
ACM Reference Format:
Surabhi S. Nath. 2020.
Hear Her Fear : Data Sonification for SensitizingSociety on Crime Against Women in India . In
IndiaHCI ’20: Proceedingsof the 11th Indian Conference on Human-Computer Interaction (IndiaHCI2020), November 5–8, 2020, Online, India.
ACM, New York, NY, USA, 6 pages.https://doi.org/10.1145/3429290.3429307
Research today demands appropriate and efficient techniques ofdata presentation for effective communication. Sonification is aversatile means of using sound for representing data. Sound canprove to be an impactful medium to represent data through itsnumerous parameters such as frequency, amplitude, timbre thatcan be adaptively controlled [20]. Sound, with its multi-dimensionalnature, can be extremely powerful as it has the potential to capturefeatures that may be missed out in a visual representation. Moreover,dynamic data are better understood through sound as its temporalnature can enable meaningful expression. In combination withvisuals, sonification is an effective form of data portrayal as it hascomplementary properties which can enhance visual presentation[7]. Further, this medium improves accessibility by reaching out tothe visually impaired population [1].Data sonification has been applied in a variety of fields includingmedicine, finance, climatology and music composition [3, 8, 10,11]. Besides the wide spectrum of use-cases, a few studies applysonification to address social concerns. Lenzi et al. in their reviewarticle discuss five different projects applying sonification to sociallyrelevant issues and compare them based on intentionality in theirdesign [13]. Emotional regulation has been attempted in a studythrough sonification of physiological data of automobile drivers toincrease self-awareness and ensure road safety [12]. Sonificationhas also been used to communicate data on alcohol health risks [19].Furfaro et al. used interactive sonification to measure emotional,perceptual and motor behaviour of individuals [4]. Work by Oh etal. used biometric data to extract the degree of sleep and heart rate,which was sonified and displayed as 3D animation for diagnosingsleep disorders [15]. Furthermore, Buckley presented a new way ofsensitizing students on risks regarding loan debts through musicalscores [2]. Our study deals with another of today’s growing societalconcerns in the Indian context – crime against women.Crime against women is seen as an issue of public health andviolation of human rights worldwide [17]. It is viewed as a serioussetback to a country’s progress and prevails across income andeducation levels globally. In India, despite some efforts to securewomen’s human rights, the situation continues to be abysmal andchallenging [18]. Data on crime against women in India is published a r X i v : . [ c s . H C ] O c t ndiaHCI 2020, November 5–8, 2020, Online, India S. S. Nath by the National Crime Records Bureau (NCRB) , Government of In-dia. As per the statistics, the total number of crimes against womenall India has increased by 70% from 2001 to 2012. In some states likeAssam, Bihar, Jharkhand, Delhi, Odisha, and many North-EasternStates, the number of crimes has more than doubled, with an al-most five times increase in West Bengal in the same period. Morerecently, the MeToo Movement in India has gained momentumwith women openly voicing the harassment they face [16]. To callattention to this situation, the International Centre for Researchon Women proposes to use media in creative ways to encourageintrospection on the social attitudes and problems of crime againstwomen in the society [9].The aim of our study is to explore the potential of data sonifi-cation for communicating the rising crime rates in Indian stateseffectively and sensitizing society on this pressing issue. We havedeveloped a user-friendly sonification interface which enables com-parison of the scenarios in various states, crime categories andyears, and identifies the critical cases demanding urgent attention.We have tested the efficacy of the designed interface by conductinga preliminary user-study that also employs sound spatialization. Tothe best of our knowledge, this is the first work on crime againstwomen using data sonification. Data on the number of crime cases against women for 35 Indianstates (including Union Territories) and All India, over 12 yearsfrom 2001 to 2012 on 9 crime categories, including total crimes wereobtained from records published by NCRB in the Open GovernmentData Platform India, under public domain . The data fields com-prised of State [1...36], Crime Category [1...9] and Year [2001...2012],resulting in a total data size of 36 x 9 x 12. Data for years subse-quent to 2012 were not available. The nine crime categories com-prise of Rape, Kidnapping & Abduction, Dowry Deaths, Assault onWomen with Intent to Outrage her Modesty, Insult to the Modestyof Women, Cruelty by Husband or Relatives, Immoral Traffic, Inde-cent Representation of Women and Total Crimes Against Women. The available data recorded the absolute number of crime casesunder the various heads. To enable meaningful interpretation of thenumber of crimes, we incorporated the population change over theyears. Due to lack of the population data of each year, the decadalpercent population growth (2001-2011) in all states published bythe Census of India was used . The population change across thetwelve years was assumed to be uniform. The yearly number ofcrime cases was proportionately altered to accommodate for thischange in each year (Algorithm 1). Further, the crime data wasnormalized by subtracting the value in the base year 2001 from thevalue in each year, which shifted the distribution to a starting valueof 0 in 2001 (Algorithm 2). ncrb.gov.in/en/crime-india data.gov.in/resources/crime-against-women-during-2001-2012 censusindia.gov.in/2011-prov-results/datafiles/india Algorithm 1:
Incorporate Population
Input :
Set of states 𝐼 and set of crime categories 𝐽 For a state 𝑖 ∈ 𝐼 , the decadal percent population growth = 𝑥 𝑖 Annual percent population growths in 2002, 2003, ..., 2011,2012 are taken to be 0 . 𝑥 𝑖 , . 𝑥 𝑖 ..., 𝑥 𝑖 , . 𝑥 𝑖 The number of crime cases for state 𝑖 in each crime category 𝑗 ∈ 𝐽 is hence reduced by 0 . 𝑥 𝑖 % , . 𝑥 𝑖 % ..., 𝑥 𝑖 % , . 𝑥 𝑖 % to getthe population incorporated number of crime cases This is repeated for all states in 𝐼 Algorithm 2:
Data Normalization
Input :
Set of states 𝐼 , set of crime categories 𝐽 and set ofyears 𝐾 For a state 𝑖 ∈ 𝐼 , crime category 𝑗 ∈ 𝐽 , number of cases inevery year 𝑥 𝑘 ( 𝑘 ∈ 𝐾 ) with first element 𝑥 = 𝑦 isnormalized as 𝑥 𝑘 = 𝑥 𝑘 − 𝑦 This is repeated for all states in 𝐼 and crime categories in 𝐽 We have used the techniques of Parameter Mapping and AuditoryIcons for data sonification.(1) Parameter mapping: It is the method of conversion of datavalues to sound parameters. It is a useful technique for con-veying data of multi-dimensional nature. In our study, wehave primarily experimented with frequencies, amplitudesand timbres since they best describe the sound and are theeasiest to perceive. Synths, or sound generating units aredeveloped to produce sound, and the parameter values arefed from the data in real-time [6].(2) Auditory Icons: These are self-explanatory real-life sounds,representative of the physical event being sonified. With asemantic content, they enable easy association and add tothe emotional perception of the event [5]. We have acquiredwomen screaming sound effects from YouTube to serve asauditory icons since they are the most natural sounds tocharacterize the pain and misery of the victims subjectedto such crimes. Six freely available unique scream soundsare used with increasing harshness in timbre. These soundswere modified using pitch shifts or amplitude factors to maphigher instances of crime with higher frequencies, largeramplitudes, harsher timbres, and vice versa.The computer interface is developed using Supercollider [14], afree and open-source programming environment used for audiosynthesis and algorithmic composition (Figure 1). Supercollideremploys a Client-Server architecture and has flexible GUI systemsto allow user interaction. The code is made available on GitHub . github/surabhisnath/Data-Sonification-Crime-Against-Women-in-Indian-States ear Her Fear : Data Sonification for Sensitizing Society on Crime Against Women in India IndiaHCI 2020, November 5–8, 2020, Online, India (a)(b)(c) Figure 1: Sonification Interface ndiaHCI 2020, November 5–8, 2020, Online, India S. S. Nath
The graphical user interface (GUI) opens with a brief introductionto the study and a dropdown to select from four sonification options(Figure 1a).The four options are grouped under two heads – Sequential DataSonification and Comparative Data Sonification for the purpose ofdiscussion. Every option leads to a new page with instructions forperforming the sonification. The GUI provides the sonified outputthrough the "Play" button, and allows switching between pagesusing the "Back" buttons on each page.
Sequential Data Sonification (Figure 1b): Performs sonifica-tion of data across the years for a particular state and crime. Theuser chooses a state and crime category. The crime data valuesacross the twelve years are sonified as screams and played in suc-cession as twelve distinct sounds. The length of each scream isaround 1 second. The user has the choice to sonify the crime dataas frequencies or as amplitudes. As frequencies, five scream timbresin increasing pitch are selected each mapped to a particular datarange. For a higher value of crime data, the scream timbre is harsherand at a higher pitch. As amplitudes, the same baseline frequencyscream sound is played in varying loudness based on the crimedata values of the twelve years. Higher data values are louder. Inboth cases, when the twelve sounds are played, the participant canidentify the patterns in the data by listening to the variations in thepitch/loudness and timbre of scream sounds. A visual graph is dis-played along with the sonified output as feedback for comparisonand validation that the data sonification is meaningful.
Comparative Data Sonification (Figure 1c): Performs sonifi-cation for comparing two crimes for a particular year and state,or for comparing two states for a particular year and crime, or forcomparing two years for a particular state and crime. The usercan fix any two variables out of state, crime category and year,and select two cases of the third variable for comparison. A singlescream sound, sonified based on both frequency and amplitudeare played along with the visuals displaying values indicating thenumber of crimes for the two cases. The scream for the larger datavalue is louder and at higher pitch. The participant can comparethe crime situations in the two cases by differentiating between thetwo screams.
For testing the outcome and evaluating the impact of sonificationfor the data on crime against women in our study, a user survey wasdesigned for participants to interact with the sonification interface,generate corresponding sonified audio outputs, and interpret thedata. User responses were collected through a questionnaire andthe effectiveness of sonification was analyzed. The survey wasconducted in a Sound Spatialization Lab equipped with 8 speakersfor multichannel audio output (Figure 2). A two-channel audiooutput can also be utilized for the study, however this setup waschosen for inducing a more immersive sonic experience.Twenty participants, a mix of 13 male and 7 female candidates,19 to 24 years of age, with homogeneous backgrounds, from under-graduate and graduate engineering programs volunteered for thesurvey. The survey was in accordance with the applicable institute policies. The participants were briefed on the purpose of the studyprior to administration. The study was conducted individually forevery participant in the lab and was approximately 10 minutes induration. The door was closed and fans were switched off to pre-vent other external sound interference. Participants were instructedto test each of the four options available on the interface at leastonce. They could choose the type of crime, state and year of theirinterest and could modify the sonification parameters and producesound output. The participants were undisturbed throughout thesession. Soon after, they answered the questionnaire and gave theirfeedback.The questionnaire consisted of 12 questions . 4 questions wereof multiple-choice type with provision for single option selection.The other 8 questions had a linear rating scale of 1 to 5, with 5 asthe most favourable rating. In our analysis, we have consideredparticipant response rating of 4 or 5 to be favourable outcomes. Figure 2: Sound Spatialization Lab
The findings from the participant responses to the questionnaire(Figure 3) reveal that data sonification can be an effective mediumfor representing data of crime against women in Indian states. 85%of the respondents concurred that the representation was mean-ingful and that they could understand the varying trends in thedata (Figure 3a). While 65% of the respondents thought that thecrime rates in states had increased on average, 30% said they os-cillated across states, crimes or years, and about 5% thought theydecreased with time (Figure 3b). The corresponding underlying dataalso shows increase, oscillation and reduction in a similar propor-tion. As sound can easily capture the temporal nature of the data,it may have been easy for respondents to decipher the changingtrend in the sonified twelve-year crime data.On questions relating to comparing two states, crime categoriesor years with respect to any one parameter, nearly 95% of the re-spondents agreed that they found comparison evident. This demon-strates that the sonification performed was reasonable and differ-ence between the two single screams was easy to comprehend. Allthe respondents agreed that the crime against women was a press-ing issue (Figure 3e) and 90% reported that the data sonification docs.google.com/forms/viewform ear Her Fear : Data Sonification for Sensitizing Society on Crime Against Women in India IndiaHCI 2020, November 5–8, 2020, Online, India (a) (b)(c) (d)(e) (f) Figure 3: User Study Findings was highly impactful (Figure 3c). On comparing sound with visualrepresentation, while 50% respondents thought that the experiencewas more impactful, 40% were somewhat unsure and 10% thoughtotherwise (Figure 3d). Considering that audio representation is aninfrequently used technique and not as mainstream as visuals, theresponse distribution is still very encouraging. The interface wasseen to be easy and self-explanatory by most respondents. Use ofsound for data representation in general was supported by abouthalf the number of participants, while the other half were mostlyunsure (Figure 3f). This is also understandable given that not alldata can be semantically represented through sound.
Although the findings of this study are promising, the work hasmultiple limitations which would be addressed in future extensions.This preliminary user study is based on a small participant set andhence making definitive conclusions is difficult. The interface de-sign is very basic and has the scope for introducing more featuressuch as pause functionalities, time monitoring, process tracing andadditional sound parameters such as tempos, distortions or reverbsto enhance the value of this study. Moreover, open-ended questionsor interviews were not included which could add greater under-standing of user-interface interaction. Further, ethical concernssuch as long-term impact of the sonification experience on theusers, particularly on victims or culprits are not addressed. Future ndiaHCI 2020, November 5–8, 2020, Online, India S. S. Nath experiments could also compare the user experience with and with-out accompanying visual data display, and evaluate the impact ofvaried sound spatialization effects.
The work establishes sound as an effective medium to representsocially relevant data. It demonstrates the potential of data sonifi-cation as an immersive user experience to effectively bring out theseverity of crime against women in Indian states. It is hoped thatthese innovations in the presentation of data will make a strongappeal and draw the attention of society to hear her fear . ACKNOWLEDGMENTS
The author thanks Prof. Timothy Moyers for his guidance andsupport, Prof. Grace Eden for her helpful suggestions and IIIT Delhifor providing access to the Sound Spatialization Lab for undertakingthis work.
REFERENCES [1] Safinah Ali, Laya Muralidharan, Felicia Alfieri, Monali Agrawal, and Jacob Jor-gensen. 2019. Sonify: Making Visual Graphs Accessible. In
International Confer-ence on Human Interaction and Emerging Technologies . Springer, 454–459.[2] Zach Buckley. 2019. Tackling the Issue of Student Debt Through Data Sonificationand Musical Scores.
Proceedings of EVA London 2019 (2019), 127–132.[3] Paolo Dell’Aversana, Gianluca Gabbriellini, and Alfonso Amendola. 2016. Sonifi-cation of geophysical data through time–frequency analysis: theory and applica-tions.
Geophysical Prospecting
65, 1 (2016), 146–157.[4] Enrico Furfaro, Frederic Bevilacqua, Nadia Berthouze, and Ana Tajadura-Jimenez.2015. Sonification of virtual and real surface tapping: evaluation of behaviorchanges, surface perception and emotional indices.
IEEE MultiMedia (2015).[5] William W Gaver. 1986. Auditory icons: Using sound in computer interfaces.
Human-computer interaction
2, 2 (1986), 167–177.[6] Florian Grond and Jonathan Berger. 2011. Parameter mapping sonification. In
The sonification handbook .[7] Thomas Hermann, Andy Hunt, and John G Neuhoff. 2011.
The sonificationhandbook . Logos Verlag Berlin.[8] Tobias Hildebrandt, Simone Kriglstein, and Stefanie Rinderle-Ma. 2012. OnApplying Sonification Methods to Convey Business Process Data.. In
CAiSEForum . Citeseer, 74–81.[9] ICRW. 2004.
Violence against women in India: A review of trends, patterns andresponses .[10] Jakob Nikolas Kather, Thomas Hermann, Yannick Bukschat, Tilmann Kramer,Lothar R Schad, and Frank Gerrit Zöllner. 2017. Polyphonic sonification ofelectrocardiography signals for diagnosis of cardiac pathologies.
Scientific reports
7, 1 (2017), 1–6.[11] Gregory Kramer, Bruce Walker, Terri Bonebright, Perry Cook, John H Flowers,Nadine Miner, and John Neuhoff. 2010. Sonification report: Status of the fieldand research agenda. (2010).[12] Steven Landry, Myounghoon Jeon, Maryam FakhrHosseini, and David Tascarella.2016. Listen to your drive: An in-vehicle sonification prototyping tool for dri-ver state and performance data. In
Adjunct Proceedings of the 8th InternationalConference on Automotive User Interfaces and Interactive Vehicular Applications .21–26.[13] Sara Lenzi and Paolo Ciuccarelli. 2020. Intentionality and design in the datasonification of social issues.
Big Data & Society
7, 2 (2020), 2053951720944603.[14] James McCartney. 2002. Rethinking the computer music language: SuperCollider.
Computer Music Journal
26, 4 (2002), 61–68.[15] Na Yea Oh, Hee Soo Kim, and Jin Wan Park. 2018. Audiovisual Expression ofBiometric Data Based on the Polysomnography Test.
TECHART: Journal of Artsand Imaging Science
5, 4 (2018), 1–5.[16] Sanjana Pegu. 2019. MeToo in India: building revolutions from solidarities.
Decision
46, 2 (2019), 151–168.[17] Nancy Felipe Russo. 2019. 12 Violence against women: A global health issue. In
Progress in Psychological Science Around the World. Volume 2: Social and AppliedIssues: Proceedings of the 28th International Congress of Psychology . Routledge.[18] Arvind Verma, Hanif Qureshi, and Jee Yearn Kim. 2017. Exploring the trend ofviolence against women in India.
International journal of comparative and appliedcriminal justice
41, 1-2 (2017), 3–18. [19] Bartlomiej P Walus, Sandra Pauletto, and Amanda Mason-Jones. 2016. Sonifica-tion and music as support to the communication of alcohol-related health risksto young people.
Journal on Multimodal User Interfaces
10, 3 (2016), 235–246.[20] David Worrall. 2019. Intelligible sonifications. In