These two positions aim to use Artificial Intelligence (AI) to optimally control adaptive multi-channel digital filters while minimizing implementation complexity. They focus on 2 distinct applications, viz. audio processing and radio broadcast interference rejection in electrical vehicles. The positions are embedded in the signal processing systems group at Eindhoven University of Technology, one of the leading AI groups in The Netherlands. They involve an intensive collaboration with NXP Semiconductors in Eindhoven, with industrial top experts from NXP providing a part of the supervision and with PhD students also being hosted by NXP on a part-time basis. As a result, the positions offer a unique combination of high-quality scientific and industrial experience.
The two positions are focused, respectively, on:
Smart digital filters for audio processing
Modern headphones, earbuds and hearing aids already have active noise cancelation. However, the user experience must be further improved. Whether a component of an audio signal contains relevant information or just irrelevant noise depends on the context. E.g., while most environmental noises must be suppressed, certain alarms or anomalous sounds might have to be passed on to the user. Or, a hearing aid should help its user to focus on a certain conversation in a setting with many simultaneous speakers (the cocktail party effect). This might be done by adaptive spectral/spatial digital filtering of multi-channel audio signals. The required filter response depends on the context. And, user feedback might be used for adaptive selection of the operation mode (listening to music, following a conversation, etc.) and personalization.
This project seeks to develop neural network-based multi-channel adaptive filters that accomplish these objectives at complexity levels consistent with ultra-low-power implementation as required e.g. for hearing aids. To be able to train these networks an existing data base of labeled audio signals will be further extended. The neural networks will efficiently predict auto-labeled target filter coefficients from extracted audio signal features. The approach will be validated via a hardware implementation and convincing real-time demos.
Smart digital filters for radio broadcast interference rejection in electrical vehicles
Interference is a common problem in wireless communications and is becoming more severe as the wireless spectrum becomes more crowded. Conventional interference rejection methods focus on radiated interferences, which originate from dedicated wireless transmitters. Recently, due to the fast development of electrical vehicles, a new vehicle-internal type of interference has emerged. For example, it is observed that DC/DC converters in electrical-cars introduce significant interference to radio receivers from the low-frequency (around 1 MHz) Amplitude Modulation (AM) signal-band all the way up to the Digital Audio Broadcasting (DAB) band (around 200 MHz). This new type of interference differs from the conventional interference in many respects. For example, it varies widely in time, frequency, and among different car models, and no statistical or deterministic interference models are available yet.
AI-based methods seem to be very suitable to counteract this type of interference. To this end this project seeks to go all the way from experimentally characterizing and modeling the interference to developing and validating AI based techniques to suppress it.
For both positions we are looking for talented, team-working-oriented and inquisitive candidates with an electrical engineering background and strong signal processing or AI skills.
Should you be interested in either or both positions then please contact Alex Alvarado.
These positions are also related to this master project.