Master Thesis Features Exploitation of Acoustic Signals Using Wavelet Networks

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Prior to feeding data to neural networks, the spectrum is typically generated using sliding windows FFT and MFCC on acoustic signal. This approach treats the acoustic signal as an image, allowing image-based neural networks, such as CNN, to perform various tasks, including keyword spotting. However, extracting temporal and frequency information from the spectrum requires heavy pre-processing due to this method, and CNN-based neural networks may be ineffective for solving such tasks.

• During your Master thesis, you will explore various approaches to leverage features present in acoustic signals. By utilizing time-encoding neural networks, the time series characteristics of acoustic signals can be better represented without the need for extensive pre-processing.
• In our team, you will investigate various inputs data representation methods and network topologies, such as wavelet networks, to analyze acoustic scenes, enabling direct processing of input into neural networks.
• Additionally, hardware design consideration will be a key factor in designing processing chains, including the design of neural networks, to ensure that the hardware implementation is feasible.

• Education: Master studies in the field of Electrical Engineering, Computer Science or comparable
• Experience and Knowledge: experience in Digital Design, (System)Verilog/VHDL, Python; background in Neural Networks
• Personality and Working Practice: you are an independent individual with a structured approach to your work
• Enthusiasm: a keen interest in future technologies and trends; a passion for innovation
• Languages: fluent in English, German is a plus

Start: according to prior agreement

Duration: 6 months

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

Need further information about the job?

Andre Guntoro (Functional Department)

+49 152 588 13129

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