Toward User-Driven Sound Recognizer Personalization with People Who Are d/Deaf or Hard of Hearing

Goodman, Steven; Liu, Ping; Jain, Dhruv; McDonnell, Emma; Froehlich, Jon E.; Findlater, Leah

doi:10.1145/3463501

Cited by 16 publications

(2 citation statements)

References 66 publications

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“…The study reported in [ 47 ] addressed the user experiences of deaf or hard-of-hearing (DHH) individuals when recording sound samples for a personalized sound recognition system. It involved 14 DHH participants in a three-part study: an initial interview, a week-long field study of recording samples, and a follow-up interview with a design probe activity.…”

Section: Methods: Personalization Of Amplification In Hearing Aidsmentioning

confidence: 99%

A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids

Tasnim,

Ni,

Lobarinas

et al. 2024

Sensors

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This paper provides a review of various machine learning approaches that have appeared in the literature aimed at individualizing or personalizing the amplification settings of hearing aids. After stating the limitations associated with the current one-size-fits-all settings of hearing aid prescriptions, a spectrum of studies in engineering and hearing science are discussed. These studies involve making adjustments to prescriptive values in order to enable preferred and individualized settings for a hearing aid user in an audio environment of interest to that user. This review gathers, in one place, a comprehensive collection of works that have been conducted thus far with respect to achieving the personalization or individualization of the amplification function of hearing aids. Furthermore, it underscores the impact that machine learning can have on enabling an improved and personalized hearing experience for hearing aid users. This paper concludes by stating the challenges and future research directions in this area.

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Section: Methods: Personalization Of Amplification In Hearing Aidsmentioning

confidence: 99%

A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids

Tasnim,

Ni,

Lobarinas

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Their proposed ProtoSound achieved an average accuracy of 88.9%. Authors in [228] also presented a review of sound recognizer tools for hearing-impaired individuals. In their review, a user-driven automated sound recognition system is studied using ML techniques.…”

Section: Healthcare Applicationsmentioning

confidence: 99%

A Survey on Artificial Intelligence-Based Acoustic Source Identification

Zaheer,

Ahmad,

Habibi

et al. 2023

IEEE Access

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The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions.

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Technical Understanding from Interactive Machine Learning Experience: a Study Through a Public Event for Science Museum Visitors

Kawabe,

Nakao,

Shitara

et al. 2024

Interacting With Computers

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While AI technology is becoming increasingly prevalent in our daily lives, the comprehension of machine learning (ML) among non-experts remains limited. Interactive machine learning (IML) has the potential to serve as a tool for end users, but many existing IML systems are designed for users with a certain level of expertise. Consequently, it remains unclear whether IML experiences can enhance the comprehension of ordinary users. In this study, we conducted a public event using an IML system to assess whether participants could gain technical comprehension through hands-on IML experiences. We implemented an interactive sound classification system featuring visualization of internal feature representation and invited visitors at a science museum to freely interact with it. By analyzing user behavior and questionnaire responses, we discuss the potential and limitations of IML systems as a tool for promoting technical comprehension among non-experts.

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Toward User-Driven Sound Recognizer Personalization with People Who Are d/Deaf or Hard of Hearing

Cited by 16 publications

References 66 publications

A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids

A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids

A Survey on Artificial Intelligence-Based Acoustic Source Identification

Technical Understanding from Interactive Machine Learning Experience: a Study Through a Public Event for Science Museum Visitors

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