Master Thesis: Task-Aware and Robust Semantic Audio Communication

Technology, Data & Digital · Software & Web Development · Software Engineering

In short

This Master Thesis project investigates task-aware representation and protection of speech information to improve communication efficiency and robustness under constrained conditions, while maintaining acceptable reconstructed speech quality. The role involves reviewing research, selecting tasks and representations, exploring prioritization methods, and comparing approaches.

Responsibilities

Review research on learned speech representations, semantic and task-aware audio communication, and robustness.
Select suitable audio-related tasks and speech representations for investigation.
Explore methods for prioritizing or protecting task-relevant speech information.
Compare task-aware and conventional approaches in terms of task performance, robustness, communication efficiency, reconstructed speech quality, and computational complexity.

Requirements

Pursuing a master’s degree, preferably in Computer Science, Electrical Engineering, Engineering Physics, or a related field.
Coursework in signal processing and machine learning is essential.
Good programming skills in MATLAB, C, or Python.
Proficient in English.

Benefits

Outstanding opportunity to use your skills and imagination to push the boundaries of what's possible.
Be challenged but not alone, joining a team of diverse innovators.
Chance to craft what comes next.

Skills

Signal ProcessingMachine LearningMATLABCPython

#Master Thesis#Audio Communication#Machine Learning#Signal Processing#Sweden

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