Master of Science Thesis Worker at APTIV Gothenburg

3 weeks ago

Gothenburg, Sweden Aptiv Full time

Master of Science Thesis Worker at APTIV Gothenburg:

APTIV is offering thesis work opportunities for 1-2 final-year Master's students with an interest in embedded software development and signal processing and with a background in Computer Science and Electrical Engineering. Students will work on a six-month project contributing to the advancement of automotive communication technologies.

Under the mentorship of experienced professionals and with access to cutting-edge resources and facilities, students will explore the world of automotive technology and work on cutting-edge research projects.

Thesis projects are aligned with APTIV's business-critical areas, so students will contribute to the company's mission of providing safe, sustainable, and personalized transportation solutions from day one.

Title:
Applicability of Machine Learning Methods in Room Impulse Response Estimation and Equalization Filters Optimization Within Automotive
 

Abstract:

Digital room equalization (EQ) improves the perceived sound quality in a listening environment influenced by the acoustical/geometrical factors. This uses digital filters to minimize the difference between the acoustic system response and a target curve. EQ filters are designed based on the room impulse response (RIR). Robust estimation of RIRs is vital for audio/speech signal processing. Measuring RIRs, for every spatial location in a 3D scene, is infeasible due to cost/complexity. Hence, reliable estimation of RIRs using computational models is of interest. Here, there are two main problems. First, to estimate the RIR and second, to compute EQ filters for that specific RIR.

Thesis description:


We explore the methods for calculation of RIR and EQ filters applicable within automotive. Specific interest is within methods which reduce the cost/complexity of the measurements while resulting in improved audio quality for people in a car cabin. This leads to multi-point EQ filters as opposed to single-point EQ filters. The thesis can be divided in two sections:

RIR Estimation: As RIR has similarities to other areas, e.g., telecommunications or image restoration, there will be a need for a literature study to draw parallels and see what types of techniques can be used. For example, compressed sensing can be used to extract models from an under-sampled sound field to enable reconstruction in target locations. Of special interest is the use of machine learning (ML) methods for this purpose. EQ Filter Calculation: After the RIR has been estimated, we need to calculate EQ filters to compensate for the room effects. Here, there is a need to study applicability of different filter design methods for various RIR models. Recently, ML models have been applied in this area and hence the aim is to perform a study of different ML methods and their effectiveness in EQ filter design.

Thesis Steps:

In this thesis, we will deal with hands-on measurements inside different rooms/cabins to get enough data which can be used to model RIR. These measurements will then be used to construct the models and also to validate them. Having validated the models, we will test different EQ filters to verify the effectiveness of the filter design methods (based on the performed literature review). The simulations/calculations will be performed in Python and different ML frameworks will be used. In general, we can think of following steps:

Study state-of-the-art literature relevant to RIR estimation and EQ filter optimization.Select at least three suitable candidates keeping in mind how to benchmark them.

Find relevant measures to be used in the benchmarking.

Implement the selected candidates (Python) so that they can be benchmarked. Perform measurements of different rooms/cabins to model/tune the RIR estimation models.

Evaluate/Compare performance of the selected RIR methods.

With the derived RIR models, find suitable EQ filters to compensate for room effects.

Evaluate/Compare performance of the selected EQ filters.

Objectives :

Objective 1: Identify/Evaluate at least three (preferably machine learning) methods for RIR estimation. Objective 2: Identify/Evaluate at least three (preferably machine learning) methods for EQ filter optimization. Objective 3: Develop/Validate a software system for RIR estimation and EQ filter optimization for automotive applications. Here, subjective/objective criteria needs to be defined and analyzed.

Out from these objectives we could together with the selected candidate define the KPI’s to be used to evaluate the performance of the thesis work. 

Additional Information:

This thesis will be performed in Gothenburg office.
If the above Thesis work matches your ambitions, be sure to apply. 
Our selection process is ongoing, and the Thesis advert will remain open until it's filled.

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