Master Thesis: Low-Delay Audio Compression Using Deep Neural Networks
Ericsson ABNy
Sammanfattning
This opportunity involves a thesis project focused on low-delay audio compression methods, utilizing deep neural networks to enhance performance. The role is based in Stockholm, Sweden, and is ideal for master's degree students in relevant fields such as Computer Science or Electrical Engineering. The project aims to improve audio compression techniques for efficient transmission and storage while maintaining sound quality.Det här erbjuder vi
Opportunity to work on innovative solutions in audio compression.Collaborate with a diverse team of innovators.Engagement in a supportive and inclusive work environment.
Ansök senast: Öppet tillsvidare
Publicerad: 2026-10-21
Beskrivning
Join our Team
About this opportunity:
Audio compression enables the efficient transmission and storage of digital audio. It is used in mobile and satellite communications, emergency services, broadcasting, and many other applications. Speech and audio compression reduces the number of bits needed to represent a signal while maintaining sound quality appropriate to the application.
Recent work has explored using powerful AI-based autoencoders in both the analysis and synthesis stages of audio compression and prediction. These approaches aim to deliver good listening quality at low bit rates of 1-2 kbps and moderate delays.
In this thesis, you will investigate low-delay audio compression methods and explore how deep neural networks (DNNs) can improve their performance. The work will consider both short- and medium-frame approaches, as well as legacy signal-processing-based codecs designed for very low latency.
What you will do:
The skills you bring:
Why join Ericsson?At Ericsson, you'll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what's possible. To build solutions never seen before to some of the world's toughest problems. You'll be challenged, but you won't be alone. You'll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.
What happens once you apply? Click Here to find all you need to know about what our typical hiring process looks like.Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer. learn more.
Primary country and city: Sweden (SE) || Stockholm
Req ID: 791812
About this opportunity:
Audio compression enables the efficient transmission and storage of digital audio. It is used in mobile and satellite communications, emergency services, broadcasting, and many other applications. Speech and audio compression reduces the number of bits needed to represent a signal while maintaining sound quality appropriate to the application.
Recent work has explored using powerful AI-based autoencoders in both the analysis and synthesis stages of audio compression and prediction. These approaches aim to deliver good listening quality at low bit rates of 1-2 kbps and moderate delays.
In this thesis, you will investigate low-delay audio compression methods and explore how deep neural networks (DNNs) can improve their performance. The work will consider both short- and medium-frame approaches, as well as legacy signal-processing-based codecs designed for very low latency.
What you will do:
- 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.
The skills you bring:
- You are 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.
- You have good programming skills in MATLAB, C, or Python.
- You are proficient in English.
Why join Ericsson?At Ericsson, you'll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what's possible. To build solutions never seen before to some of the world's toughest problems. You'll be challenged, but you won't be alone. You'll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.
What happens once you apply? Click Here to find all you need to know about what our typical hiring process looks like.Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer. learn more.
Primary country and city: Sweden (SE) || Stockholm
Req ID: 791812
Ansök till tjänsten
Master Thesis: Low-Delay Audio Compression Using Deep Neural Networks
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OM FÖRETAGET
Ericsson AB
