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At Emobot, I led research on automatic speech emotion recognition — pushing accuracy significantly through synthetic data augmentation via emotion conversion, with direct impact on a real-time healthcare application.
Short description of portfolio item number 2 
Short description of portfolio item number 1
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My PhD project at Télécom Paris and Deezer, studying how listening context — activity, mood, device, time of day — shapes what music people want to hear, and building systems that learn to predict it automatically.
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For my M.Sc. at NUS, I studied what makes song lyrics easy or hard to understand, and built systems to measure it automatically — motivated by a real application: recommending music for language learning.
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My first research project, spanning my M.Sc. at Nile University and an internship at Sony Stuttgart. The goal: automatically separate the direct sound from the diffuse ambience in a stereo recording, to enable surround sound upmixing.
