다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The organization of behavior: A neuropsychological theory

Thank you for downloading an introduction to the event related potential technique. Maybe you have knowledge that, people have look hundreds times for their favorite readings like this an introduction to the event related potential technique, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious bugs inside their laptop.

An Introduction To The Event Related Potential Technique

Post-processing methods can be used in mobile communications to improve the intelligibility of speech in adverse near-end background noise conditions. Generally, it is assumed that the input of the post-processing contains quantization noise only, that is to say, no far-end noise is present. However, this assumption is not entirely realistic. Therefore, the effect of farend noise with and without noise reduction on the performance of three post-processing methods is studied in this investigation. The performance evaluation is done using subjective intelligibility and quality tests in several far-end and near-end noise conditions. The results suggest that although the noise reduction generally improves performance in stationary far-end noise, the noise reduction does not improve intelligibility in unstationary far-end noise conditions but has a positive impact on perceptual quality for some of the post-processing methods.

Intelligibility enhancement at the receiving end of the speech transmission system - effects of far-end noise reduction

The composite autoregressive system can be used to estimate a speech source-filter decomposition in a rigorous manner, thus having potential use in glottal inverse filtering. By introducing a suitable prior, spectral tilt can be introduced into the source component estimation to better correspond to human voice production. However, the current expectationmaximisation based composite autoregressive model optimisation leaves room for improvement in terms of speed. Inspired by majorisation-minimisation techniques used for nonnegative matrix factorisation, this work derives new update rules for the model, resulting in faster convergence compared to the original approach. Additionally, we present a new glottal inverse filtering method based on the composite autoregressive system and compare it with inverse filtering methods currently used in glottal excitation modelling for parametric speech synthesis. These initial results show that the proposed method performs comparatively well, sometimes outperforming the reference methods.

/pdf/majorisation-minimisation-based-optimisation-of-the-1b31o1nvpo.pdf

Majorisation-minimisation based optimisation of the composite autoregressive system with application to glottal inverse filtering

An all-pole modeling technique, Linear Prediction with Lowfrequency Emphasis (LPLE), which emphasizes the lower frequency range of speech, is presented. The method is based on first interpreting conventional linear predictive (LP) analyses of successive prediction orders with parallel structures using the concept of symmetric linear prediction. In these implementations, symmetric linear prediction is preceded by simple pre-filters, which are of either low or high frequency characteristics. Combining those symmetric linear predictors that are not preceded by high-frequency pre-filters yields the LPLE predictor. It is proved that the all-pole filters computed by LPLE are always stable. The results show that the method is well-suited when low-order all-pole models with improved modeling of the lowest formants are needed.

Linear predictive method with low-frequency emphasis

This paper presents an enhancement system for early stage Spanish Esophageal Speech (ES) vowels. The system decomposes the input ES into neoglottal waveform and vocal tract filter components using Iterative Adaptive Inverse Filtering (IAIF). The neoglottal waveform is further decomposed into fundamental frequency F0, Harmonic to Noise Ratio (HNR), and neoglottal source spectrum. The enhanced neoglottal source signal is constructed using a natural glottal flow pulse computed from real speech. The F0 and HNR are replaced with natural speech F0 and HNR. The vocal tract formant frequencies (spectral peaks) and bandwidths are smoothed, the formants are shifted downward using second order frequency warping polynomial and the bandwidth is increased to make it close to the natural speech. The system is evaluated using subjective listening tests on the Spanish ES vowels /a/, /e/, /i/, /o/, /u/. The Mean Opinion Score (MOS) shows significant improvement in the overall quality (naturalness and intelligibility) of the vowels. Index Terms: speech enhancement, glottal flow, analysis synthesis vocal tract, spectral sharpening, warping

/pdf/vowel-enhancement-in-early-stage-spanish-esophageal-speech-3iurywg7tg.pdf

Paavo Alku

Papers

Intelligibility enhancement at the receiving end of the speech transmission system - effects of far-end noise reduction

Majorisation-minimisation based optimisation of the composite autoregressive system with application to glottal inverse filtering

Linear predictive method with low-frequency emphasis

Vowel Enhancement in Early Stage Spanish Esophageal Speech Using Natural Glottal Flow Pulse and Vocal Tract Frequency Warping

Refining a deep learning-based formant tracker using linear prediction methods