Joachim Thiemann
Papers
1
Total Citations
3
H-Index
1
About
Joachim Thiemann is a researcher whose work sits at the intersection of audio signal processing and machine learning, with a particular focus on spatial audio and binaural hearing. His key research areas include sound source localization, robot audition, and real-time audio processing architectures. Thiemann is best known for his contributions to probabilistic localization methods, most notably the real-time implementation of a Gaussian mixture model (GMM)-based binaural localization algorithm on a VLIW-SIMD processor. This work, published in 2017, demonstrated how complex statistical models could be efficiently deployed on embedded hardware—a critical step for applications in acoustic navigation, teleconferencing, and speaker tracking. While his most-cited paper has garnered 3 citations, its significance lies in bridging the gap between theoretical probabilistic frameworks and practical, low-latency systems. Thiemann’s research has helped advance the field of robot audition, enabling machines to better understand and interact with their acoustic environments. His work continues to influence the development of robust, real-time spatial audio systems for both robotics and human-computer interaction.
Research Focus
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Top Papers
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