Optimization and evaluation of sigmoid function with a priori SNR estimate for real-time speech enhancement

2013 ◽  
Vol 55 (2) ◽  
pp. 358-376 ◽  
Author(s):  
Pei Chee Yong ◽  
Sven Nordholm ◽  
Hai Huyen Dam
Keyword(s):  
Real Time ◽  
Speech Enhancement ◽  
A Priori ◽  
Sigmoid Function

scholarly journals Noise Estimation and Suppression Using Nonlinear Function withA PrioriSpeech Absence Probability in Speech Enhancement

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Soojeong Lee ◽  
Gangseong Lee
Keyword(s):  
Speech Enhancement ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Nonlinear Function ◽  
Noise Estimation ◽  
Noise Power ◽  
Sigmoid Function ◽  
Noisy Environments ◽  
Adaptive Parameter ◽  
Residual Noise

This paper proposes a noise-biased compensation of minimum statistics (MS) method using a nonlinear function anda priorispeech absence probability (SAP) for speech enhancement in highly nonstationary noisy environments. The MS method is a well-known technique for noise power estimation in nonstationary noisy environments; however, it tends to bias noise estimation below that of the true noise level. The proposed method is combined with an adaptive parameter based on a sigmoid function anda prioriSAP for residual noise reduction. Additionally, our method uses an autoparameter to control the trade-off between speech distortion and residual noise. We evaluate the estimation of noise power in highly nonstationary and varying noise environments. The improvement can be confirmed in terms of signal-to-noise ratio (SNR) and the Itakura-Saito Distortion Measure (ISDM).

scholarly journals Monitoring distributed computing beyond the traditional time-series histogram

2020 ◽  
Vol 245 ◽  
pp. 03036
Author(s):  
M S Doidge ◽  
P. A. Love ◽  
J Thornton
Keyword(s):  
Time Series ◽  
Distributed Computing ◽  
Real Time ◽  
A Priori ◽  
Time Data ◽  
Use Of Time ◽  
Computing Services ◽  
Monitoring Tools ◽  
Novel Approach ◽  
Current Monitoring

In this work we describe a novel approach to monitor the operation of distributed computing services. Current monitoring tools are dominated by the use of time-series histograms showing the evolution of various metrics. These can quickly overwhelm or confuse the viewer due to the large number of similar looking graphs. We propose a supplementary approach through the sonification of real-time data streamed directly from a variety of distributed computing services. The real-time nature of this method allows operations staff to quickly detect problems and identify that a problem is still ongoing, avoiding the case of investigating an issue a-priori when it may already have been resolved. In this paper we present details of the system architecture and provide a recipe for deployment suitable for both site and experiment teams.

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