Speech Target Extraction Expert
Vor 6 Tagen
**Targeted Speech Understanding in Multi-Source Auditory Environments**
In today's hearing acoustics applications, such as ear buds, hearing aids, smart watches, and in-car acoustics, targeted speech understanding is crucial for delivering exceptional user experiences. To address this challenge, AI-based systems, including recurrent neural networks, LSTMs, and beamformer networks, are being deployed to perform noise cancellation, speaker enhancement, and other advanced audio processing tasks.
**Your Mission**
In this Master thesis, you will investigate target speech extraction with different background noises and develop a target extraction method based on directives. You will evaluate speech extraction methods for hearing aids, focusing on their performance in various auditory environments.
**Key Responsibilities**
- Develop and implement data augmentation techniques on toy datasets to enhance the robustness of target speech extraction methods.
- Implement pre-processing stages of audio to improve the quality of input signals.
- Design and train neural networks to extract target speech based on directives.
- Extend the developed method to multi-source speech targets, enabling the system to handle complex auditory environments.
- Collaborate with cross-functional teams to integrate the developed method into real-world applications.
**Requirements**
- Master's degree in Electrical Engineering, Computer Science, or a related field.
- Proficiency in Python and C++ programming languages.
- Background in neural networks and experience with PyTorch is an advantage.
- Independent, structured, and highly motivated individual with a keen interest in future technologies and trends.
- Fluency in English; German is an advantage.
**Additional Information**
This thesis requires enrollment at a university. Please attach your CV, transcript of records, examination regulations, and if indicated, a valid work and residence permit.
Bosch values diversity and inclusion and welcomes applications from all qualified candidates, regardless of gender, age, disability, religion, ethnic origin, or sexual identity.
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