Name: ETHNOCENTRIC BIAS & HUMAN-COMPUTER INTERACTION, MUSIC & Machine Learning
Start: 2024-04-22T18:00:00-07:00
End: 2024-04-22T20:00:00-07:00
Location: SEMI

\*\* REGISTER [HERE](https://events.vtools.ieee.org/m/411486) \*\*

***Synopsis:***
The first talk investigates the challenges faced by refugee populations globally, emphasizing ethnocentric biases. Focusing on Syrian refugees and contrasting media coverage of the Ukrainian crisis, the talk describes how to leverage empirical research in human-computer interaction (HCI). The key goal of the project that will be discussed is to assess bias among Ukrainian and Syrian refugees using eye-tracking technology to study participants' decision-making and analyze pupil size data. The talk will investigate whether biases observed in media portrayal of refugees can be detected through HCI research.
The second talk presents insights into understanding music using machine learning to analyze and categorize various aspects of music. The talk will explore how music is digitally represented using features analogous to building a fingerprint for each song. The presentation then explores various dimensionality reduction techniques for music feature analysis. The speakers present a novel approach using representation learning in lower dimensions to evaluate the efficacy of mel-spectrogram features in capturing music characteristics across diverse languages. By visualizing the features in transformed spaces, the speakers explain how insights can be gained into fine-grained attributes like vocalist timbre, gender, language, and industry prominence. Spectral and non-spectral algorithms, including PCA, t-SNE, and UMAP, are employed to analyze music datasets. The speakers’ findings demonstrate UMAP's superior performance in discerning subtle musical nuances. The research paper that the speakers co-authored on a similar topic won the best paper award at the 10th ICMC, a Springer conference in 2024. The audience will discover how machines are learning the language of music, opening doors for new music exploration and analysis tools.

Susan Delafuente

IEEE SCV Women in Engineering

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ETHNOCENTRIC BIAS & HUMAN-COMPUTER INTERACTION, MUSIC & Machine Learning

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IEEE SCV Women in Engineering

ETHNOCENTRIC BIAS & HUMAN-COMPUTER INTERACTION, MUSIC & Machine Learning

IEEE SCV Women in Engineering

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