Master Thesis Features Exploitation of Acoustic Signals Using Wavelet Networks

Robert Bosch Group; Renningen; Vor Ort; EUR 40.000 - 60.000; Vollzeit

Zusammenfassung

The Robert Bosch GmbH invites applications for a Master's thesis focused on exploiting acoustic signals using wavelet networks. Ideal candidates will have a background in Electrical Engineering or Computer Science, familiar with Digital Design and Neural Networks. The position offers a unique opportunity to research cutting-edge technology in a collaborative environment, shaping the future of signal processing.

Qualifikationen

• Master studies in Electrical Engineering, Computer Science, or a comparable field required.
• Experience in Digital Design, Python, and Neural Networks preferred.
• Fluent in English; German is a plus.

Aufgaben

• Explore approaches to leverage features in acoustic signals using time-encoding neural networks.
• Investigate input data representation methods and network topologies for acoustic scene analysis.
• Consider hardware design in neural network architecture for feasible implementation.

Kenntnisse

Digital Design

Python

Neural Networks

Ausbildung

Master studies in Electrical Engineering or Computer Science

Tools

Verilog

VHDL

Jobbeschreibung

Master Thesis Features Exploitation of Acoustic Signals Using Wavelet Networks

• Full-time

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

The Robert Bosch GmbH is looking forward to your application!

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

In our team, you will investigate different input 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 considerations will be important in designing processing chains, including neural network architectures, to ensure feasible hardware implementation.

• Education: Master studies in Electrical Engineering, Computer Science, or a comparable field
• Experience and Knowledge: Experience in Digital Design, (System)Verilog/VHDL, Python; background in Neural Networks
• Personality and Working Practice: Independent, structured approach to work
• Enthusiasm: Keen interest in future technologies and trends; passion for innovation
• Languages: Fluent in English; German is a plus

Start: According to prior agreement

Duration: 6 months

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

Diversity and inclusion are integral to our culture. We welcome all applications regardless of gender, age, disability, religion, ethnicity, or sexual orientation.

Need further information about the job?
Andre Guntoro (Functional Department)
[phone]