担子菌Phanerochaete chrysosporium由来GHファミリー6に属するセロビオヒドロラーゼの構造と機能解析

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 鮫島 正浩, 東京大学教授 岩田 忠久, 東京大学教授 伏信 進矢, 東京大学准教授 五十嵐 圭日子, 東京大学准教授 横山 朝哉University of Tokyo(東京大学

Sparsity and cosparsity for audio declipping: a flexible non-convex approach

This work investigates the empirical performance of the sparse synthesis versus sparse analysis regularization for the ill-posed inverse problem of audio declipping. We develop a versatile non-convex heuristics which can be readily used with both data models. Based on this algorithm, we report that, in most cases, the two models perform almost similarly in terms of signal enhancement. However, the analysis version is shown to be amenable for real time audio processing, when certain analysis operators are considered. Both versions outperform state-of-the-art methods in the field, especially for the severely saturated signals

Voice activity detection based on density ratio estimation and system combination

Abstract-We propose a robust voice activity detection (VAD) based on density ratio estimation. In highly noisy environments, the likelihood ratio test (LRT) is effective. Conventional LRT estimates both speech and noise models, calculates the likelihood of each model, and uses ratios of such likelihood to detect speech. However, in LRT, the likelihood ratio of speech and noise models is required, whereas likelihood of individual models is not necessarily required. The framework of the density ratio estimation models likelihood ratio functions by a kernel and directly generates a likelihood ratio. Applying density ratio estimation to VAD requires that feature selection and noise adaptation must be considered. This is because the density ratio estimation constrains the shape of the likelihood ratio functions and speech is dynamic. This paper addresses these problems. To improve accuracy, the proposed method is combined with conventional LRT. Experimental results using CENSREC-1-C show that the proposed method is more effective than conventional methods, especially in non-stationary noisy environments

Revisiting Synthesis Model of Sparse Audio Declipper

The state of the art in audio declipping has currently been achieved by SPADE (SParse Audio DEclipper) algorithm by Kiti\'c et al. Until now, the synthesis/sparse variant, S-SPADE, has been considered significantly slower than its analysis/cosparse counterpart, A-SPADE. It turns out that the opposite is true: by exploiting a recent projection lemma, individual iterations of both algorithms can be made equally computationally expensive, while S-SPADE tends to require considerably fewer iterations to converge. In this paper, the two algorithms are compared across a range of parameters such as the window length, window overlap and redundancy of the transform. The experiments show that although S-SPADE typically converges faster, the average performance in terms of restoration quality is not superior to A-SPADE

The third 'CHiME' speech separation and recognition challenge: Analysis and outcomes

This paper presents the design and outcomes of the CHiME-3 challenge, the first open speech recognition evaluation designed to target the increasingly relevant multichannel, mobile-device speech recognition scenario. The paper serves two purposes. First, it provides a definitive reference for the challenge, including full descriptions of the task design, data capture and baseline systems along with a description and evaluation of the 26 systems that were submitted. The best systems re-engineered every stage of the baseline resulting in reductions in word error rate from 33.4% to as low as 5.8%. By comparing across systems, techniques that are essential for strong performance are identified. Second, the paper considers the problem of drawing conclusions from evaluations that use speech directly recorded in noisy environments. The degree of challenge presented by the resulting material is hard to control and hard to fully characterise. We attempt to dissect the various 'axes of difficulty' by correlating various estimated signal properties with typical system performance on a per session and per utterance basis. We find strong evidence of a dependence on signal-to-noise ratio and channel quality. Systems are less sensitive to variations in the degree of speaker motion. The paper concludes by discussing the outcomes of CHiME-3 in relation to the design of future mobile speech recognition evaluations

Protocol for analyzing enzymatic hydrolysis of cellulose using surface pitting observation technology

Summary: Assaying enzymatic degradation of water-insoluble substrates like cellulose is challenging because only the substrate surface is accessible to the enzymes resulting in low reaction rates. Here, we describe a protocol for surface pitting observation technology (SPOT), an ultra-sensitive quantitative assay for analyzing enzymatic hydrolysis of cellulose. We describe the use of a porous substrate to accelerate the hydrolysis rate of cellulose. We also detail the steps for combining inkjet patterning and optical profilometry to analyze volume loss upon hydrolysis.For complete details on the use and execution of this protocol, please refer to Tsudome et al. (2022).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics