Master Thesis: Low-Delay Audio Compression Using Deep Neural Networks

Teknik, data och digitalt · Mjukvaru- och webbutveckling · Mjukvaruutveckling

I korthet

An opportunity to conduct a Master Thesis on low-delay audio compression using deep neural networks. You will investigate and enhance existing methods by incorporating DNN-based analysis and synthesis, optimizing for hardware with limited resources. Requires a Master's degree in a relevant field, coursework in signal processing and machine learning, and programming skills in MATLAB, C, or Python.

Ansvarsområden

Review existing mono, low-rate audio compression methods using short and medium frame lengths (10–20 ms).
Review legacy signal-processing-based low-latency codecs using frame lengths of 1–2.5 ms.
Select a promising core low-delay compression method.
Enhance the selected method by incorporating DNN-based analysis and synthesis.
Optimize the resulting algorithm for deployment on hardware with limited computational resources.

Krav

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.

Förmåner

Outstanding opportunity to use your skills and imagination to push the boundaries of what's possible.
Opportunity to build solutions never seen before to some of the world's toughest problems.
Be challenged, but not alone.
Join a team of diverse innovators.

Färdigheter

MATLABCPythonDeep Neural Networkssignal processingmachine learning

#Audio Compression#Deep Neural Networks#Machine Learning#Signal Processing#Master Thesis

Om Ericsson

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