Sound Intelligence: How AI Can Promote Communities and Deter Crime with proper focus on marginalized groups.

Jan 23, 2024

Introduction

Sound is a powerful source of information that can reveal the health, diversity, and activity of a neighborhood. From the chirping of birds to the laughter of children at play, sound can indicate the presence and quality of life in a community. However, sound can also signal danger, such as gunshots, sirens, or screams, that require immediate attention and response. How can we leverage sound data to better understand and improve our neighborhoods, and prevent crime and violence including underrepresented communities?

This white paper proposes the use of artificial intelligence (AI) to analyze the soundscapes of neighborhoods across the US and various cities, and to derive insights and recommendations for enhancing community euphoria, which would contribute to crime prevention and community policing. I argue that AI can go beyond the existing technology of shot spotter, which monitors and detects gunshots, and provide a more holistic and proactive approach to neighborhood improvement and safety. I outline the benefits, challenges, and potential applications of sound intelligence, and call for further research and collaboration in this emerging field.

What is Sound Intelligence?

Sound intelligence is the application of AI to analyze, interpret, and generate sound data, and to use it for various purposes and outcomes[1][2][3][4]. Sound intelligence can be used to monitor and detect events, such as gunshots, explosions, or accidents, and to alert the authorities or the public[5][6]. It can also be used to measure and evaluate the biodiversity and social dynamics of a neighborhood, such as the diversity and abundance of wildlife, the level and type of human activity, the mood and sentiment of the residents, and the quality and frequency of social interactions[7][8]. Sound intelligence can also be used to create and enhance sound environments, such as by generating natural or ambient sounds, masking unwanted noises, or providing feedback and guidance[9][10][11].

Sound intelligence relies on the collection and processing of sound data[12][13][14][15], which can be obtained from various sources, such as microphones, sensors, smartphones, or social media[16][17][18][19][20]. Sound data can be analyzed using various AI techniques, such as machine learning, deep learning, natural language processing, speech recognition, and sound synthesis[21][22][23][24]. Sound data can be represented and visualized using various methods, such as spectrograms, waveforms, or maps[25][26][27][28]. Sound data can also be integrated and correlated with other types of data, such as geospatial, demographic, or behavioral data[29][30][31], to provide a richer and more comprehensive picture of a neighborhood.

Why Sound Intelligence Matters for Neighborhoods and Crime Prevention

Sound intelligence can provide valuable insights and benefits for neighborhoods and crime prevention, such as:

· It can help assess the health and diversity of the natural environment, and identify the presence and patterns of wildlife, such as birds, insects, or mammals[32]. This can help monitor and protect the biodiversity and ecosystem services of a neighborhood, and inform conservation and restoration efforts[33][34].

· It can help measure and evaluate the social and cultural dynamics of a neighborhood, and identify the level and type of human activity, such as traffic, commerce, recreation, or education[35][36]. This can help understand and improve the livability and vibrancy of a neighborhood, and inform urban planning and development[37][38].

· It can help gauge and influence the mood and sentiment of the residents, and identify the indicators and triggers of positive or negative emotions, such as happiness, anger, fear, or stress[39][40][41][42]. This can help promote and enhance the well-being and happiness of a neighborhood, and inform mental health and social services[43][44][45][46].

· It can help detect and prevent crime and violence, and identify the sources and locations of potential threats, such as gunshots, screams, or alarms[47][48][49][50][51][52]. This can help alert and respond to emergencies, and inform public safety and law enforcement[53][54][55][56][57][58].

· It can help foster and facilitate community engagement and participation, and identify the opportunities and channels for communication, collaboration, and feedback[59][60][61][62]. This can help build and strengthen the social capital and cohesion of a neighborhood, and inform civic education and empowerment[63][64][65][66].

· By analyzing the soundscapes of neighborhoods, sound intelligence can help identify areas that may be underserved or neglected, and inform targeted interventions to improve the quality of life in these communities[67][68][69].

Sound intelligence can also help monitor and detect incidents of crime or violence, and alert the authorities or the public, potentially improving safety in underrepresented communities[70][71][72][73][74][75].
By engaging residents of underrepresented communities in the design and implementation of sound intelligence, it can help foster community engagement and participation, and empower residents to have a voice in shaping their neighborhoods[76][77][78].
Sound intelligence can also help measure and evaluate the social and cultural dynamics of a neighborhood, providing insights into the needs and concerns of underrepresented communities, and informing policies and programs to address these issues[79].

Sound intelligence can also provide a more holistic and proactive approach to neighborhood improvement and safety, by going beyond the reactive and narrow focus of shot spotter, and by addressing the underlying and interconnected factors that affect the quality of life and the risk of crime in a neighborhood[80][81][82][83][84][85]. By using sound intelligence, we can create more healthy, diverse, lively, happy, and safe neighborhoods, and prevent crime and violence before they occur[86][87][88][89][90][91].

How to Implement Sound Intelligence for Neighborhoods and Crime Prevention

Sound intelligence is an emerging and interdisciplinary field that requires further research and collaboration among various stakeholders, such as researchers, practitioners, policymakers, and residents[92][93][94]. Some of the possible steps and actions to implement sound intelligence for neighborhoods and crime prevention are:

· Develop and deploy sound sensors and devices in strategic and ethical locations across neighborhoods, and collect and store sound data in secure and accessible platforms[95][96][97].

· Apply and improve AI techniques to analyze and interpret sound data, and to generate and synthesize sound data, and ensure the accuracy, reliability, and validity of the results[98][99][100][101].

· Create and use sound maps and dashboards to visualize and communicate sound data and insights, and to enable interactive and user-friendly exploration and manipulation of the data[102][103][104][105].

· Integrate and correlate sound data with other types of data, such as geospatial, demographic, or behavioral data, and use data fusion and analytics to provide a more comprehensive and nuanced understanding of a neighborhood[106][107].

· Use sound data and insights to inform and support decision-making and policy-making for neighborhood improvement and safety, and to evaluate and monitor the impact and outcomes of the interventions[108][109][110].

· Engage and involve the residents and the community in the design and implementation of sound intelligence, and solicit their feedback and input, and ensure their privacy and consent[111][112][113].

· Share and disseminate sound data and insights with the public and the media[114][115][116], and raise awareness and education about the value and potential of sound intelligence.

· Collaborate and coordinate with other stakeholders and partners, such as academia, industry, government, or civil society, and leverage their expertise and resources, and foster innovation and best practices[117][118][119][120].

Conclusion

Sound intelligence is a vital and emerging field that can enhance our understanding and decision-making in various domains and contexts. It can help us monitor and protect the environment, improve health and well-being, optimize urban planning and mobility, and enrich our cultural and artistic expression. However, sound intelligence also poses some ethical and social challenges that need to be addressed with care and responsibility. Therefore, I propose the following recommendations:

Develop and implement sound ethical principles and guidelines for sound intelligence, and ensure transparency, accountability, and fairness in its design and use.

Protect the privacy and security of sound data and users, and respect their rights and preferences, and provide them with meaningful control and consent mechanisms.

Share and disseminate sound data and insights with the public and the media, and raise awareness and education about the value and potential of sound intelligence.

Collaborate and coordinate with other stakeholders and partners, such as academia, industry, government, or civil society, and leverage their expertise and resources, and foster innovation and best practices.

By following these recommendations, we can ensure that sound intelligence serves the public good and enhances the quality of life for all.

[1] OpenAI. (2020). Jukebox. Retrieved from https://openai.com/research/jukebox

[2] Zewe, A. (2022). Using sound to model the world. MIT News. Retrieved from https://news.mit.edu/2022/sound-model-ai-1101

[3] Technology Review. (2022). Google’s new AI can hear a snippet of song—and then keep on playing. Retrieved from https://www.technologyreview.com/2022/10/07/1060897/ai-audio-generation/

[4]Sloan Review. (2023). Sound Business: The Promise of Audio Machine Learning Technologies. Retrieved from https://sloanreview.mit.edu/article/listen-up-machine-learning-holds-great-promise-for-audio-applications/

[5] OpenAI. (2020). Jukebox. Retrieved from https://openai.com/research/jukebox

[6] Zewe, A. (2022). Using sound to model the world. MIT News. Retrieved from https://news.mit.edu/2022/sound-model-ai-1101

[7] Zewe, A. (2022). Using sound to model the world. MIT News. Retrieved from https://news.mit.edu/2022/sound-model-ai-1101

[8] Technology Review. (2022). Google’s new AI can hear a snippet of song—and then keep on playing. Retrieved from https://www.technologyreview.com/2022/10/07/1060897/ai-audio-generation/

[9] OpenAI. (2020). Jukebox. Retrieved from https://openai.com/research/jukebox

[10] Technology Review. (2022). Google’s new AI can hear a snippet of song—and then keep on playing. Retrieved from https://www.technologyreview.com/2022/10/07/1060897/ai-audio-generation/

[11] Sloan Review. (2023). Sound Business: The Promise of Audio Machine Learning Technologies. Retrieved from https://sloanreview.mit.edu/article/listen-up-machine-learning-holds-great-promise-for-audio-applications/

[12] iSixSigma. (2010). Building a Sound Data Collection Plan. Retrieved from https://www.isixsigma.com/sampling-data/building-sound-data-collection-plan/

[13] Hugging Face. (2022). A Complete Guide to Audio Datasets. Retrieved from https://moon-ci-docs.huggingface.co/blog/audio-datasets

[14] Zapproved. (n.d.). What is Forensically Sound Data Collection? Retrieved from https://zapproved.com/blog/what-is-forensically-sound-data-collection/#:~:text=Forensically%20sound%20data%20collection%20refers%20to%20the%20process,be%20defensible%3A%20consistent%2C%20repeatable%2C%20well%20documented%2C%20and%20authenticated.

[15] AIMultiple. (2024). Audio Data Collection for AI: Challenges & Best Practices in 2024. Retrieved from https://research.aimultiple.com/audio-data-collection/

[16] Hugging Face. (2022). A Complete Guide to Audio Datasets. Retrieved from https://moon-ci-docs.huggingface.co/blog/audio-datasets

[17] Soundcharts. (2022). Best Music Data Guideline 2022 - 52 Music Data APIs. Retrieved from https://soundcharts.com/blog

[18] Medium. (2023). From Noise to Knowledge: Curating an Audio Dataset for Machine Learning. Retrieved from https://medium.com/@Gts.AI/from-noise-to-knowledge-curating-an-audio-dataset-for-machine-learning-56a017a3393d

[19] Twine. (n.d.). The Ultimate List of 100+ Audio and Video Datasets. Retrieved from https://www.twine.net/blog/ultimate-list-100-audio-and-video-datasets/

[20] Twine. (2021). 150+ Audio and Video Open Datasets. Retrieved from https://www.twine.net/blog/100-audio-and-video-datasets/?ref=dataphoenix.info

[21] AltexSoft. (2022). Audio Analysis With Machine Learning: Building AI-Fueled Sound Detection App. Retrieved from https://www.altexsoft.com/blog/audio-analysis/

[22] Frontiersin. (2023). Advanced AI methods for audio event analysis. Retrieved from https://www.frontiersin.org/research-topics/41186/audio-event-detection-recognition-and-monitoring-with-ai

[23] Matthewrenze. (2021). AI for Audio Analysis. Retrieved from https://matthewrenze.com/articles/ai-for-audio-analysis/

[24] Luzmo. (2024). Using AI for Data Analysis: The Ultimate Guide. Retrieved from https://www.luzmo.com/blog/ai-data-analysis

[25] Siu, A. F., Kim, G. S., O’Modhrain, S., & Follmer, S. (2022). Supporting Accessible Data Visualization Through Audio Data Narratives. CHI '22: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Hamburg, Germany. Retrieved from https://alexasiu.com/publications/siu2022datanarratives.pdf

[26] Manal, H., Abdellah, E., & Said, B. A. (2023). Accessible Data Representation with Natural Sound. CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Hamburg, Germany. Retrieved from https://dl.acm.org/doi/fullHtml/10.1145/3544548.3581087

[27] SoundMap. (n.d.). A Visualization Tool to Explore Multi-Attribute Sound Data. Retrieved from https://www.cs.ubc.ca/~tmm/courses/547-21/projects/elizabeth-mifta-nichole/update.pdf

[28] Medium. (n.d.). Investigating Audio Data Visualization for Interactive Sound Recognition. Retrieved from https://dl.acm.org/doi/abs/10.1145/3377325.3377483

[29] MDPI. (2022). Data Augmentation and Deep Learning Methods in Sound Classification: A Systematic Review. Retrieved from https://www.mdpi.com/2079-9292/11/22/3795

[30] SpringerLink. (2023). Deep Learning for Image and Sound Data: An Overview. Retrieved from https://link.springer.com/chapter/10.1007/978-3-031-27762-7_27

[31] BMJ. (2010). Three techniques for integrating data in mixed methods studies. Retrieved from https://www.bmj.com/content/341/bmj.c4587

[32] Rainforest Connection. (2023). Harnessing the Power of Sound and AI to Track Global Biodiversity Framework (GBF) Targets. Retrieved from https://rfcx.org/publications/harnessing-the-power-of-sound-and-ai-to-track-global-biodiversity-framework-gbf-targets

[33] Rainforest Connection. (2023). Harnessing the Power of Sound and AI to Track Global Biodiversity Framework (GBF) Targets. Retrieved from https://rfcx.org/publications/harnessing-the-power-of-sound-and-ai-to-track-global-biodiversity-framework-gbf-targets

[34] Wildlife Acoustics. (n.d.). Ecosystem, Biodiversity, or Soundscape Monitoring. Retrieved from https://www.wildlifeacoustics.com/solutions/ecosystem-biodiversity-or-soundscape-monitoring

[35] Urban Intelligence Lab. (n.d.). Neighborhood dynamics and inequality. Retrieved from https://www.urbanintelligencelab.org/research/neighborhood-dynamics-and-inequality/

[36] MDPI. (n.d.). Mapping the Soundscape in Communicative Forms for Cultural Heritage. Retrieved from https://www.mdpi.com/2571-9408/4/4/248

[37] Urban Intelligence Lab. (n.d.). Neighborhood dynamics and inequality. Retrieved from https://www.urbanintelligencelab.org/research/neighborhood-dynamics-and-inequality/

[38] MDPI. (n.d.). Mapping the Soundscape in Communicative Forms for Cultural Heritage. Retrieved from https://www.mdpi.com/2571-9408/4/4/248

[39] Doerrfeld, B. (2015). 20+ Emotion Recognition APIs That Will Leave You Impressed, and Concerned. Nordic APIs. Retrieved from https://nordicapis.com/20-emotion-recognition-apis-that-will-leave-you-impressed-and-concerned/

[40] Somers, M. (2019). Emotion AI, explained. MIT Sloan. Retrieved from https://mitsloan.mit.edu/ideas-made-to-matter/emotion-ai-explained

[41] Cognigy. (n.d.). How Conversational AI Detect Emotions With Sentiment Analysis. Retrieved from https://www.cognigy.com/blog/sentiment-analysis

[42] Smithsonian Magazine. (n.d.). How Algorithms Discern Our Mood From What We Write Online. Retrieved from https://www.smithsonianmag.com/innovation/how-algorithms-discern-our-mood-what-we-write-online-180975840/

[43] Doerrfeld, B. (2015). 20+ Emotion Recognition APIs That Will Leave You Impressed, and Concerned. Nordic APIs. Retrieved from https://nordicapis.com/20-emotion-recognition-apis-that-will-leave-you-impressed-and-concerned/

[44] Somers, M. (2019). Emotion AI, explained. MIT Sloan. Retrieved from https://mitsloan.mit.edu/ideas-made-to-matter/emotion-ai-explained

[45] Cognigy. (n.d.). How Conversational AI Detect Emotions With Sentiment Analysis. Retrieved from https://www.cognigy.com/blog/sentiment-analysis

[46] Smithsonian Magazine. (n.d.). How Algorithms Discern Our Mood From What We Write Online. Retrieved from https://www.smithsonianmag.com/innovation/how-algorithms-discern-our-mood-what-we-write-online-180975840/

[47] Sound Intelligence. (n.d.). Sound Intelligence. Retrieved from https://soundintel.com/

[48] Sound Intelligence. (n.d.). Aggression Detection. Retrieved from https://www.soundintel.com/products/overview/aggression/

[49] ASIS International. (n.d.). Audio and Acumen Against Aggression. Retrieved from https://www.asisonline.org/security-management-magazine/articles/2021/03/audio-and-acumen-against-aggression/

[50] National Institute of Justice. (n.d.). Using Forensic Intelligence To Combat Serial and Organized Violent Crimes. Retrieved from https://nij.ojp.gov/topics/articles/using-forensic-intelligence-combat-serial-and-organized-violent-crimes

[51] National Institute of Justice. (n.d.). Using Forensic Intelligence To Combat Serial and Organized Violent Crimes. Retrieved from https://nij.ojp.gov/topics/articles/using-forensic-intelligence-combat-serial-and-organized-violent-crimes

[52] National Institute of Justice. (n.d.). Using Forensic Intelligence To Combat Serial and Organized Violent Crimes. Retrieved from https://nij.ojp.gov/topics/articles/using-forensic-intelligence-combat-serial-and-organized-violent-crimes

[53] Sound Intelligence. (n.d.). Sound Intelligence. Retrieved from https://soundintel.com/

[54] Sound Intelligence. (n.d.). Aggression Detection. Retrieved from https://www.soundintel.com/products/overview/aggression/

[55] ASIS International. (n.d.). Audio and Acumen Against Aggression. Retrieved from https://www.asisonline.org/security-management-magazine/articles/2021/03/audio-and-acumen-against-aggression/

[56] National Institute of Justice. (n.d.). Using Forensic Intelligence To Combat Serial and Organized Violent Crimes. Retrieved from https://nij.ojp.gov/topics/articles/using-forensic-intelligence-combat-serial-and-organized-violent-crimes

[57] National Institute of Justice. (n.d.). Using Forensic Intelligence To Combat Serial and Organized Violent Crimes. Retrieved from https://nij.ojp.gov/topics/articles/using-forensic-intelligence-combat-serial-and-organized-violent-crimes

[58] National Institute of Justice. (n.d.). Using Forensic Intelligence To Combat Serial and Organized Violent Crimes. Retrieved from https://nij.ojp.gov/topics/articles/using-forensic-intelligence-combat-serial-and-organized-violent-crimes

[59] CultureHive. (n.d.). How your organisation can engage with underrepresented groups. Retrieved from https://www.culturehive.co.uk/digital-heritage-hub/resource/leadership/digital-methods-engage-underrepresented-groups/

[60] The Annie E. Casey Foundation. (n.d.). Fostering Resident Voice and Influence. Retrieved from https://www.aecf.org/resources/fostering-resident-voice-and-influence

[61] Advocacy and Communication Solutions. (n.d.). 12 Tips for Community Engagement. Retrieved from https://www.advocacyandcommunication.org/wp-content/uploads/2020/05/ACS_Community_Engagement_Tips.pdf

[62] Hearts Agency. (n.d.). How Emotional Intelligence Drives Community Engagement. Retrieved from https://hearts.agency/how_emotional_intelligence_drives_community_engagement/

[63] CultureHive. (n.d.). How your organisation can engage with underrepresented groups. Retrieved from https://www.culturehive.co.uk/digital-heritage-hub/resource/leadership/digital-methods-engage-underrepresented-groups/

[64] The Annie E. Casey Foundation. (n.d.). Fostering Resident Voice and Influence. Retrieved from https://www.aecf.org/resources/fostering-resident-voice-and-influence

[65] Advocacy and Communication Solutions. (n.d.). 12 Tips for Community Engagement. Retrieved from https://www.advocacyandcommunication.org/wp-content/uploads/2020/05/ACS_Community_Engagement_Tips.pdf

[66] Hearts Agency. (n.d.). How Emotional Intelligence Drives Community Engagement. Retrieved from https://hearts.agency/how_emotional_intelligence_drives_community_engagement/

[67] U.S. Census Bureau. (2021). Census Data Equity Initiative for Underserved Communities. Retrieved from https://www.census.gov/newsroom/press-releases/2021/data-equity.html

[68] U.S. Census Bureau. (2022). Census Data Tools for Equity and Identifying Underserved Communities. Retrieved from https://www.census.gov/data/academy/webinars/2022/data-tools-for-equity-and-underserved-communities.html

[69] Federal Housing Finance Agency. (n.d.). Underserved Areas Data. Retrieved from https://www.fhfa.gov/DataTools/Downloads/Pages/Underserved-Areas-Data.aspx

[70] Sound Intelligence. (n.d.). Sound Intelligence. Retrieved from https://soundintel.com/

[71] Sound Intelligence. (n.d.). Aggression Detection. Retrieved from https://www.soundintel.com/products/overview/aggression/

[72] ASIS International. (n.d.). Audio and Acumen Against Aggression. Retrieved from https://www.asisonline.org/security-management-magazine/articles/2021/03/audio-and-acumen-against-aggression/

[73] National Institute of Justice. (n.d.). Using Forensic Intelligence To Combat Serial and Organized Violent Crimes. Retrieved from https://nij.ojp.gov/topics/articles/using-forensic-intelligence-combat-serial-and-organized-violent-crimes

[74] National Institute of Justice. (n.d.). Using Forensic Intelligence To Combat Serial and Organized Violent Crimes. Retrieved from https://nij.ojp.gov/topics/articles/using-forensic-intelligence-combat-serial-and-organized-violent-crimes

[75] National Institute of Justice. (n.d.). Using Forensic Intelligence To Combat Serial and Organized Violent Crimes. Retrieved from https://nij.ojp.gov/topics/articles/using-forensic-intelligence-combat-serial-and-organized-violent-crimes

[76] CultureHive. (n.d.). How your organisation can engage with underrepresented groups. Retrieved from https://www.culturehive.co.uk/digital-heritage-hub/resource/leadership/digital-methods-engage-underrepresented-groups/

[77] STAR Library Network. (n.d.). Challenges and Strategies for Engaging Underserved and Underrepresented Audiences in Informal STEM Learning: Lessons Learned from Project BUILD. Retrieved from https://www.starnetlibraries.org/engaging-underserved-audiences/

[78] American Psychological Association. (2021). There’s a new push to reach underserved communities. Retrieved from https://www.apa.org/monitor/2021/01/trends-underserved-communities

[79] Springer. (n.d.). Re-envisioning Intelligence in Cultural Context. Retrieved from https://link.springer.com/chapter/10.1007/978-3-030-92798-1_13

[80] Sound Intelligence. (n.d.). Sound Intelligence. Retrieved from https://soundintel.com/

[81] Sound Intelligence. (n.d.). Aggression Detection. Retrieved from https://www.soundintel.com/products/overview/aggression/

[82] ASIS International. (n.d.). Audio and Acumen Against Aggression. Retrieved from https://www.asisonline.org/security-management-magazine/articles/2021/03/audio-and-acumen-against-aggression/

[83] National Institute of Justice. (n.d.). Using Forensic Intelligence To Combat Serial and Organized Violent Crimes. Retrieved from https://nij.ojp.gov/topics/articles/using-forensic-intelligence-combat-serial-and-organized-violent-crimes

[84] National Institute of Justice. (n.d.). Using Forensic Intelligence To Combat Serial and Organized Violent Crimes. Retrieved from https://nij.ojp.gov/topics/articles/using-forensic-intelligence-combat-serial-and-organized-violent-crimes

[85] National Institute of Justice. (n.d.). Using Forensic Intelligence To Combat Serial and Organized Violent Crimes. Retrieved from https://nij.ojp.gov/topics/articles/using-forensic-intelligence-combat-serial-and-organized-violent-crimes

[86] Sound Intelligence. (n.d.). Sound Intelligence. Retrieved from https://soundintel.com/

[87] Sound Intelligence. (n.d.). Aggression Detection. Retrieved from https://www.soundintel.com/products/overview/aggression/

[88] ASIS International. (n.d.). Audio and Acumen Against Aggression. Retrieved from https://www.asisonline.org/security-management-magazine/articles/2021/03/audio-and-acumen-against-aggression/

[89] National Institute of Justice. (n.d.). Using Forensic Intelligence To Combat Serial and Organized Violent Crimes. Retrieved from https://nij.ojp.gov/topics/articles/using-forensic-intelligence-combat-serial-and-organized-violent-crimes

[90] National Institute of Justice. (n.d.). Using Forensic Intelligence To Combat Serial and Organized Violent Crimes. Retrieved from https://nij.ojp.gov/topics/articles/using-forensic-intelligence-combat-serial-and-organized-violent-crimes

[91] National Institute of Justice. (n.d.). Using Forensic Intelligence To Combat Serial and Organized Violent Crimes. Retrieved from https://nij.ojp.gov/topics/articles/using-forensic-intelligence-combat-serial-and-organized-violent-crimes

[92] Bosch Global. (n.d.). Audio AI: Learning to understand sounds. Retrieved from https://www.bosch.com/research/research-fields/artificial-intelligence/audio-ai/

[93] Stanford Medicine Magazine. (n.d.). Putting sound and acoustics to work in medicine. Retrieved from https://stanmed.stanford.edu/innovations-helping-harness-sound-acoustics-healing/

[94] Twine. (2020, November 11). The most exciting audio AI research projects of 2020. Retrieved from https://www.twine.net/blog/audio-ai-research-projects/

[95] Science Connected Magazine. (2019, May 10). Sound Around Town Uses Data to Combat Noise Pollution. Retrieved from https://magazine.scienceconnected.org/2019/05/sound-around-town-noise-pollution/

[96] Communications of the ACM. (2019, February). SONYC: A System for Monitoring, Analyzing, and Mitigating Urban Noise Pollution. Retrieved from Communications of the ACM.

[97] Electronic Products. (2022, January 10). Designer’s guide for deploying sensors in smart cities. Retrieved from https://cacm.acm.org/magazines/2019/2/234354-sonyc/fulltext

[98] AltexSoft. (2022, May 12). Audio Analysis With Machine Learning: Building AI-Fueled Sound Detection App. Retrieved from https://www.altexsoft.com/blog/audio-analysis/

[99] Engineer Your Sound. (n.d.). How Does AI Recognize Sound? (How machines listen and hear). Retrieved from https://engineeryoursound.com/how-does-ai-recognize-sound-how-machines-listen-and-hear/

[100] : Hivo. (n.d.). Enhance Audio Quality Using AI: Comprehensive Guide. Retrieved from https://hivo.co/blog/enhancing-audio-quality-with-ai-a-comprehensive-guide

[101] Matthewrenze. (2021, June 1). AI for Audio Analysis. Retrieved from https://matthewrenze.com/articles/ai-for-audio-analysis/

[102] Cyanite.ai. (2022, January 11). Making Sense of Music Data - Data Visualizations. Retrieved from https://cyanite.ai/2022/01/11/making-sense-of-music-data-data-visualizations/

[103] Soundmaps.app. (n.d.). Sound world mapper. Retrieved from https://soundmaps.app/

[104] Kapwing. (n.d.). Online Music Visualizer: Add Sound Waves to Any Video. Retrieved from https://www.kapwing.com/tools/music-visualizer

[105] Speak Ai. (2023, January 1). How To Visualize Sound. Retrieved from https://speakai.co/how-to-visualize-sound/

[106] AltexSoft. (2022, May 12). Audio Analysis With Machine Learning: Building AI-Fueled Sound Detection App. Retrieved from https://www.altexsoft.com/blog/audio-analysis/

[107] Sensative. (n.d.). Combining multiple different data sets for more insights. Retrieved from https://sensative.com/iot_use_cases/combining-multiple-different-data-sets-for-more-insights/

[108] CSG Justice Center. (n.d.). Use of Data to Inform Decision Making. Retrieved from https://csgjusticecenter.org/publications/expanding-first-response/the-toolkit/use-of-data-to-inform-decision-making/

[109] OECD iLibrary. (n.d.). Toward sound problem identification, policy formulation and design. Retrieved from https://www.oecd-ilibrary.org/sites/c03e01b3-en/1/3/3/1/index.html?itemId=/content/publication/c03e01b3-en&_csp_=a1a5231b133b7248263fae9d659a3b6b&itemIGO=oecd&itemContentType=book

[110] Issues. (n.d.). Helping Communities Use Data to Make Better Decisions. Retrieved from https://issues.org/helping-communities-use-data-to-make-better-decisions/

[111] GJCPP. (2018). Community Engagement: Using Feedback Loops to Empower Residents and Influence Systemic Change in Culturally Diverse Communities. Retrieved from https://www.gjcpp.org/en/article.php?issue=30&article=179

[112] National Civic League. (n.d.). Human-Centered Design and Community Engagement. Retrieved from https://www.nationalcivicleague.org/ncr-article/human-centered-design-and-community-engagement/

[113] SpringerLink. (n.d.). Community Engagement in Design and Planning. Retrieved from https://link.springer.com/chapter/10.5822/978-1-61091-036-1_19

[114] OECD iLibrary. (n.d.). Evidence-based and data-driven public communication. Retrieved from https://www.oecd-ilibrary.org/sites/97680e10-en/index.html?itemId=/content/component/97680e10-en

[115] Harvard University. (2023, February). Ten simple rules for innovative dissemination of research. Retrieved from https://www.hsph.harvard.edu/global-health-research-partnership/wp-content/uploads/sites/2448/2023/02/innovative-dissemination.pdf

[116] BMC Public Health. (2023). Dissemination of public health research to prevent non-communicable diseases. Retrieved from https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-023-15622-x

[117] MeisterTask. (n.d.). Stakeholder Collaboration | Why It’s Important And How It Works. Retrieved from https://www.meistertask.com/blog/why-good-stakeholder-collaboration-matters-and-how-to-get-it-right/

[118] Canada.ca. (n.d.). Collaboration with Partners and Stakeholders. Retrieved from https://www.canada.ca/en/revenue-agency/corporate/careers-cra/information-moved/cra-competencies-standardized-assessment-tools/canada-revenue-agency-competencies-april-2016/collaboration-partners-stakeholders.html

[119] Forbes. (2022, October 26). 15 Effective Tips For Improving Communication With Stakeholders. Retrieved from https://www.forbes.com/sites/forbesnonprofitcouncil/2022/10/26/15-effective-tips-for-improving-communication-with-stakeholders/?sh=47f9e7bb4cc7

[120] I2Insights. (2021, November 25). Stakeholder engagement primer: 7. Listening and dialogue. Retrieved from https://i2insights.org/2021/11/25/listening-and-dialogue/