Thesis Researcher in Automotive Signal Processing
4 weeks ago
APTIV is providing thesis opportunities for 1-2 final-year Master's students interested in embedded software engineering and signal processing, with backgrounds in Computer Science and Electrical Engineering. Students will engage in a six-month project aimed at advancing automotive communication technologies.
Under the guidance of seasoned professionals and with access to state-of-the-art resources and facilities, students will delve into the realm of automotive technology and participate in innovative research projects.
Title:
Frequency Domain Adaptive Filtering (FDAF) in Acoustic Echo Cancellation (AEC) for Automotive Applications
Abstract:
Acoustic echo cancellation (AEC) is crucial for ensuring clear and echo-free communication in automotive settings. However, the computational requirements of linear convolutional filters, especially due to their extended impulse responses, present significant challenges for low-power processors. Additionally, the use of adaptive Infinite Impulse Response (IIR) filters often encounters limitations such as local minima and instabilities. Although Fast Fourier Transforms (FFTs) have become more efficient, block processing and frequency domain adaptive filters (FDAFs) are now standard on low-power DSPs, even for automotive systems that do not strictly require low-power processors.
Thesis Description:
This thesis proposal aims to assess and develop an FDAF-based AEC system and subsequently integrate this algorithm into Aptiv's proprietary DSP framework, SoundCubeTM. Your research will also involve defining key performance indicators (KPIs) and utilizing various performance measurement techniques to evaluate the resulting algorithm. Given that the SoundCube framework is primarily written in C (embedded), this project will require a significant amount of C programming and a comprehensive understanding of adaptive filters.
We are looking for two final-year Master's students who are eager to undertake a six-month thesis project. The students will collaborate on embedded software development and signal processing algorithm development, employing tools like Matlab or Python to meet our objectives.
Introduction:
Acoustic echo cancellation (AEC) is an essential element in automotive communication systems. However, traditional AEC solutions face computational hurdles, particularly when managing long impulse responses. Typical AEC applications can involve systems with impulse responses exceeding 100ms in length, necessitating adaptive Finite Impulse Response (FIR) filters with over 1000 coefficients. This imposes considerable demands on low-power processors, highlighting the need to explore alternative solutions, such as Frequency Domain Adaptive Filters (FDAF). Furthermore, while adaptive Infinite Impulse Response (IIR) filters are computationally less intensive, they often struggle with local minima and instabilities during filter adaptation.
Our research seeks to tackle these challenges by evaluating and developing an AEC system based on FDAF. This approach typically offers faster convergence speeds and reduced complexity, making it well-suited for automotive systems characterized by rapidly changing signal paths. To validate the effectiveness of this approach, we will integrate the developed FDAF-based AEC algorithm into Aptiv's SoundCubeTM DSP framework, a platform widely utilized in the automotive industry.
2. Objectives:
The primary objectives of this thesis proposal are as follows:
Algorithm Development:
Develop an efficient AEC algorithm based on Frequency Domain Adaptive Filters (FDAF) capable of addressing the challenges associated with automotive environments and integrate the FDAF-based AEC algorithm into Aptiv's SoundCubeTM DSP framework.
Performance Evaluation:
Establish Key Performance Indicators (KPIs) to evaluate the effectiveness of the FDAF-based AEC algorithm and utilize various performance measurement techniques to benchmark the algorithm against existing solutions.
3. Methodology:
Our approach will involve a combination of embedded software development in C and signal processing algorithm development in Matlab or Python. This multi-faceted methodology will encompass the following steps:
3.1. Algorithm Development:
Research and design an FDAF-based AEC algorithm optimized for automotive environments. Implement the algorithm in C for seamless integration into the SoundCubeTM framework.
3.2. Performance Evaluation:
Define a comprehensive set of KPIs to measure the efficacy of the AEC system. Employ Matlab or Python to conduct extensive performance testing and benchmarking.
4. Expected Results:
Upon successful completion of this thesis project, we anticipate the following outcomes:
The development of an FDAF-based AEC algorithm capable of effectively addressing challenges associated with automotive communications systems. Followed by the integration of the algorithm into Aptiv's SoundCubeTM DSP framework, making it accessible for practical communication systems and a set of well-defined KPIs and benchmarking results to assess the performance of the FDAF-based AEC system.
5. Conclusion:
This thesis proposal outlines our research plan to enhance Acoustic Echo Cancellation for automotive environments using Frequency Domain Adaptive Filters. By integrating this algorithm into Aptiv's SoundCubeTM framework, we aim to improve the clarity and quality of audio communication within vehicles.
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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