Job Title
Master Thesis – Core Tech Media
Job Description
We have four ideas for you to pick from :
1) Knowledge distillation for audio classification task
2) Evaluating Generated and Simulated Training Data for Sound Event Classification
3) Energy-Efficient Audio Classification for (Battery-Powered) Edge Devices
4) Lightweight Speaker Anonymization for Intercoms
Category
Computer Science, Applied Mathematics, Technical Physics, Electronics Engineering or similar
Scope
Two Master students, 30hp each
Idea 1 - Knowledge distillation for audio classification task
Knowledge distillation is a machine learning technique that transfers knowledge from a large, complex model (the teacher) to a smaller, more compact model (the student).
Our Audio Classification model runs on our camera products (“edge hardware”). Therefore it is a smaller model, many order of magnitudes smaller than state-of-the-art classification models capable of running on big GPUs.
Relevant courses
C/Python, Machine Learning/AI
Goals
Explore using larger models on PC to improve a smaller model, to boost performance above what should be possible with the on edge model sizes.
Idea 2 - Evaluating Generated and Simulated Training Data for Sound Event Classification
Audio classification can detect events like glass breaking, gunshots, screams, or alarms. These events share multiple problems though: they are rare, hard to record, and often privacy-sensitive.
Synthetic data offers a way around this bottleneck. Three families of methods have matured:
Procedural/physical simulation: Clean event recordings are combined with simulated room acoustics, for example room impulse responses from software tools like pyroomacoustics. Microphone characteristics and background noise are added on top.
Soundscape synthesis: Libraries such as Scaper mix foreground events into background scenes with controlled timing, SNR, and overlap.
Generative models: Text-to-audio models such as AudioLDM, AudioGen, and Stable Audio generate novel event samples from prompts.
However, the extent to which each approach generalizes to real-world conditions, the proportion of real data it can substitute, and the circumstances under which synthetic data is detrimental remain largely unexplored.
Relevant courses
C/Python, Machine Learning/AI
Goals
Research Questions:
How does classifier performance on real-world test data change when real training data is partially or fully replaced by synthetic data?
Which synthesis strategy (physical simulation, soundscape mixing, or generative models) gives the best real-world generalization per unit of effort?
Can a small amount of real data combined with synthetic pre-training match a model trained on a much larger real dataset?
Can we predict the domain gap in advance, for example with Fréchet Audio Distance (FAD), and does FAD correlate with downstream accuracy?
Expected Contributions:
A systematic, reproducible comparison of synthetic audio strategies for rare-event classification.
Practical guidelines on how much real data is still needed and which synthesis method to use.
A pipeline for generating and evaluating synthetic audio datasets.
Idea 3 - Energy-Efficient Audio Classification for (Battery-Powered) Edge Devices
Audio classification can detect glass breaking, gunshots, aggression, or machinery faults, which makes it a strong complement to video in security and IoT systems. Running these models continuously on battery-powered devices is impractical because always-on listening drains energy quickly. Most research optimizes for accuracy or latency. Far less addresses the full energy budget of the sensing pipeline, from microphone to decision. This thesis focuses on that gap.
How can a battery-powered device run continuous audio classification while minimizing average power consumption and keeping detection performance acceptable?
The average power of an always-on system can be modeled as: Here, is the duty cycle, and is the active power of each pipeline stage…