Takehiko Mizoguchi

Papers

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About

Takehiko Mizoguchi has made impactful contributions to the field of audio scene classification, a critical technology for smart sensing in robotics, medical monitoring, surveillance, and autonomous vehicles. His most-cited work, "At the Speed of Sound: Efficient Audio Scene Classification" (2020), introduces a novel retrieval-based architecture that integrates recurrent neural networks with attention mechanisms to compute efficient audio embeddings. This approach enables rapid and accurate classification of acoustic environments, addressing the computational constraints of real-world, resource-limited platforms. With 5 citations, this paper has established Mizoguchi as a key figure in advancing practical, low-latency audio analysis. His research bridges the gap between deep learning efficiency and real-time deployment, offering scalable solutions for intelligent systems that must interpret their surroundings through sound. Mizoguchi’s work is particularly notable for its focus on balancing accuracy with speed, a challenge central to the next generation of autonomous and assistive technologies.