Glenn G. Ko

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There is a good amount of similarity between source separation approaches that use spectrograms captured from multiple microphones and computer vision algorithms that use multiple images for segmentation problems. Just as one would use Markov random fields (MRF) to solve image segmentation problems, we propose a method of modeling source separation using(More)
Compute-intensive applications are emerging in intelligent home, retail store and automotive industries. These applications are becoming more sophisticated with new features rich in audio, video, image, and machine learning capabilities that demand heavy computations. We present the EMERALD (EMERging Applications and algorithms for Low power Device)(More)
We explore sound source separation to isolate human voice from background noise on mobile phones, e.g. talking on your cell phone in an airport. The challenges involved are real-time execution and power constraints. As a solution, we present a novel hardware-based sound source separation implementation capable of real-time streaming performance. The(More)
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