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Mi Suk Lee

2 accepted papers

2020

A Dual-Staged Context Aggregation Method towards Efficient End-to-End Speech Enhancement

ICASSP 2020accepted

In speech enhancement, an end-to-end deep neural network converts a noisy speech signal to a clean speech directly in the time domain without time-frequency transformation or mask estimation. However, aggregating contextual information from a high-resolution time domain signal with an affordable mod…

Cited by 0Source ↗Scholar ↗
2020

Efficient and Scalable Neural Residual Waveform Coding with Collaborative Quantization

ICASSP 2020accepted

Scalability and efficiency are desired in neural speech codecs, which supports a wide range of bitrates for applications on various devices. We propose a collaborative quantization (CQ) scheme to jointly learn the codebook of LPC coefficients and the corresponding residuals. CQ does not simply shoeh…

Cited by 0Source ↗Scholar ↗