Master Thesis – Core Tech Media

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

Computer Science, Applied Mathematics, Technical Physics, Electronics Engineering or similar

Two Master students, 30hp each

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.

C/Python, Machine Learning/AI

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.

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.

Research questions:

• Cascaded detection: How much energy does a multi-stage pipeline save? Such a pipeline would use an ultra-low-power stage, such as energy thresholding or a tiny neural network, to gate a larger classifier.
• Model compression: How does quantization (int8 or lower), pruning, and knowledge distillation affect the trade-off between accuracy and energy per inference?
• Front-end optimization: Can cheaper features or lower sample rates and frame rates cut computation without a significant accuracy loss? Examples include fewer mel bands, learned filterbanks, or raw-waveform models.
• Adaptive operation: Can the system change its duty cycle or model size based on context, such as time of day, background noise, or battery level?

Expected contributions:

• A benchmark that measures accuracy against real energy use for compressed audio models on embedded hardware.
• Design guidelines for cascaded, always-on audio pipelines, quantifying where energy is actually spent.
• A prototype adaptive system showing longer battery life with minimal loss in detection quality.

Research Questions:

• Privacy–utility trade-off: How do lightweight anonymization methods compare with state-of-the-art VoicePrivacy baselines in identity protection and speech intelligibility?
• Edge feasibility: Which methods meet real-time constraints (latency, memory, and compute) on representative embedded hardware, and what does it cost in privacy or quality?
• Attacker robustness: How well do lightweight methods hold up against an informed attacker who knows the anonymization method and adapts their speaker verification system to it?
• Domain robustness (optional): How do intercom conditions, such as narrowband audio, codecs, background noise, and reverberation, affect both anonymization strength and intelligibility?

C/Python, Machine Learning/AI

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 (sensing, feature extraction, inference, wake-up, communication). Lowering means reducing either how often each stage runs or how much power it draws while running.

C/Python, Signal Processing, Machine Learning/AI

You are valuable to us – how nice that you are interested in one of our proposals! There are a few things for you to keep in mind when applying.

• Applications are accepted in both Swedish and English, and you apply via the proposal advert.
• The announced thesis is open only to students affiliated with a Swedish University/College either directly or via an exchange program.
• When the thesis proposal states that it includes two students working together, we would like you to apply in pairs. In these cases, send one application each but make sure to clearly state in your application who your co-applicant is. If you have any questions regarding this, please do not hesitate to contact us.
• Please attach your CV and University/College grade summary.

Core Technologies Media Manager Daniel Andersson at Daniel.B.Andersson@axis.com

Certain roles at Axis require background checks, which means applicable verifications will be done in these recruitments. Notice will be provided before we take any action.

We enable a smarter and safer world by creating innovative solutions for improving security and business performance. As a network technology company and industry leader, we offer solutions in video surveillance, access control, intercom, and audio systems, enhanced by intelligent analytics applications.

With around 6,000 committed employees in over 50 countries, we collaborate with partners worldwide. Together, we thrive in our friendly, open, and collaborative culture and inspire each other to think beyond the expected. United by our commitment to inclusion, diversity, and sustainability, we consistently seek to develop our skills and way of working.

For more information about Axis, please visit our website www.axis.com.

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