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Analysis

This paper explores dereverberation techniques for speech signals, focusing on Non-negative Matrix Factor Deconvolution (NMFD) and its variations. It aims to improve the magnitude spectrogram of reverberant speech to remove reverberation effects. The study proposes and compares different NMFD-based approaches, including a novel method applied to the activation matrix. The paper's significance lies in its investigation of NMFD for speech dereverberation and its comparative analysis using objective metrics like PESQ and Cepstral Distortion. The authors acknowledge that while they qualitatively validated existing techniques, they couldn't replicate exact results, and the novel approach showed inconsistent improvement.
Reference

The novel approach, as it is suggested, provides improvement in quantitative metrics, but is not consistent.

OpenAI Baselines: DQN

Published:May 24, 2017 07:00
1 min read
OpenAI News

Analysis

The article announces the open-sourcing of OpenAI Baselines, a project to reproduce reinforcement learning algorithms. The initial release focuses on DQN and its variants. This is significant for researchers and practitioners in the field of reinforcement learning as it provides accessible and reproducible implementations.
Reference

We’re open-sourcing OpenAI Baselines, our internal effort to reproduce reinforcement learning algorithms with performance on par with published results. We’ll release the algorithms over upcoming months; today’s release includes DQN and three of its variants.