Generative Audio Systems Using Power-Preserving All-Pass Filters

2016 ◽  
Vol 40 (1) ◽  
pp. 54-69
Author(s):  
Greg Surges ◽  
Tamara Smyth ◽  
Miller Puckette
Keyword(s):  
Strong Coupling ◽  
Phase Distortion ◽  
Time Varying ◽  
Matrix Formulation ◽  
Pass Filter ◽  
Feedback Network ◽  
High Level ◽  
Time Invariant ◽  
Audio System ◽  
Over Time

This article describes the use of second-order all-pass filters as components in a feedback network, with parameters made time varying to enable effects such as phase distortion in a generative audio system. The term “audio” is used here to distinguish from generative “music” systems, emphasizing the strong coupling between processes governing the production of high-level music and lower-level audio. The classical time-invariant implementation of an all-pass filter is subject to issues of instability that can arise when time-invariant filter parameters are allowed to vary over time. These instabilities are examined, along with the adoption of a power-preserving rotation matrix formulation of the all-pass filter to ensure stability and ultimately an improved synthesis for a generative audio system.

scholarly journals Power of Moran’s I Test for Spatial Dependence in Panel Data Models with Time Varying Spatial Weights Matrices

2015 ◽  
Vol 3 (5) ◽  
pp. 463-471 ◽  
Author(s):  
Bianling Ou ◽  
Xin Zhao ◽  
Mingxi Wang
Keyword(s):  
Panel Data ◽  
Spatial Dependence ◽  
Moran’S I ◽  
Panel Data Models ◽  
Time Varying ◽  
Moran's I ◽  
Spatial Weights ◽  
Spatial Weights Matrix ◽  
Time Invariant ◽  
Over Time

AbstractThe spatial weights matrix is usually specified to be time invariant. However, when it are constructed with economic/socioeconomic distance, trade /demographic/climatic characteristics, these characteristics might be changing over time, and then the spatial weights matrix substantially varies over time. This paper focuses on power of Moran’s I test for spatial dependence in panel data models with where spatial weights matrices can be time varying (TV-Moran). Compared with Moran’s I test with time invariant spatial weights matrices (TI-Moran), the empirical power of TV-Moran test for spatial dependence are evaluated. Our extensive Monte Carlo simulation results indicate that Moran’s I test with misspecified time invariant spatial weights matrices is questionable; Instead, TV-Moran test has shown superiority in higher power, especially for cases with negative spatial correlation parameters and the large time dimension.

scholarly journals Trade and Income—Exploiting Time Series in Geography

2019 ◽  
Vol 11 (4) ◽  
pp. 1-35 ◽  
Author(s):  
James Feyrer
Keyword(s):  
Time Series ◽  
Fixed Effects ◽  
Time Varying ◽  
Omitted Variables ◽  
Trade Growth ◽  
Cross Country ◽  
Time Invariant ◽  
Variable Problem ◽  
Positive Effect ◽  
Over Time

Establishing a robust causal relationship between trade and income has been difficult. Frankel and Romer (1999) uses a geographic instrument to identify a positive effect of trade on income. Rodriguez and Rodrik (2001) shows that these results are not robust to controlling for omitted variables such as distance to the equator or institutions. This paper solves the omitted variable problem by generating a time-varying geographic instrument. Improvements in aircraft technology have caused the quantity of world trade carried by air to increase over time. Country pairs with relatively short air routes compared to sea routes benefit more from this change in technology. This heterogeneity can be used to generate a geography-based instrument for trade that varies over time. The time-series variation allows for controls for country fixed effects, eliminating the bias from time-invariant variables such as distance from the equator or historically determined institutions. Trade has a significant effect on income with an elasticity of roughly one-half. Differences in predicted trade growth can explain roughly 17 percent of the variation in cross-country income growth between 1960 and 1995. (JEL F14, F43, L93)

scholarly journals Does Contextual Change Affect Basic Human Values? A Dynamic Comparative Multilevel Analysis Across 32 European Countries

2017 ◽  
Vol 48 (4) ◽  
pp. 490-510 ◽  
Author(s):  
Raül Tormos ◽  
Christin-Melanie Vauclair ◽  
Henrik Dobewall
Keyword(s):  
Income Inequality ◽  
Contextual Effects ◽  
Time Varying ◽  
Unemployment Rates ◽  
Openness To Change ◽  
National Wealth ◽  
Self Transcendence ◽  
Time Invariant ◽  
Contextual Change ◽  
Over Time

This article examines the relationship of stable contextual differences and contextual change with the endorsement of Schwartz’s (1992) two basic value dimensions—Openness-to-Change versus Conservation and Self-Enhancement versus Self-Transcendence. Using six waves of the European Social Survey, an extension of multilevel analysis is used which combines both a cross-national comparative and a dynamic analysis of values. The hierarchical data structure and the covariates for value endorsement are defined at three distinct levels: a first level for individuals (with sociodemographic variables, such as age and gender), a second level for country-waves (with time-varying covariates), and a third level for country (with time-invariant covariates). The main aim is to determine if changes in contextual covariates over time are related to value differences between countries over and above contextual time-invariant covariates. High national wealth and low income inequality predicted high Self-Transcendence values and low Conservation values. Low national unemployment rates were associated with less conservatism. When entered simultaneously into the model, only time-invariant differences in gross domestic product (GDP) remained to be a significant predictor of Schwartz’s two basic value dimensions. Finally, we found that an increase in income inequality over time has a certain incremental effect on the endorsement of Conservation over Openness-to-Change values. There were no associations for changes in national wealth and unemployment rates, suggesting that for value endorsement, time-varying contextual effects are less important overall than time-invariant contextual effects.

Robustness Over Time-Varying Channels in DNN-HMM ASR Based Human-Robot Interaction

2017 ◽  
Author(s):  
José Novoa ◽  
Jorge Wuth ◽  
Juan Pablo Escudero ◽  
Josué Fredes ◽  
Rodrigo Mahu ◽  
...  
Keyword(s):  
Human Robot Interaction ◽  
Time Varying ◽  
Robot Interaction ◽  
Time Varying Channels ◽  
Over Time

scholarly journals Epidemic Processes Over Time-Varying Networks

2018 ◽  
Vol 5 (3) ◽  
pp. 1322-1334 ◽  
Author(s):  
Philip E. Pare ◽  
Carolyn L. Beck ◽  
Angelia Nedic
Keyword(s):  
Time Varying ◽  
Time Varying Networks ◽  
Over Time

scholarly journals An Adaptive All-Pass Filter for Time-Varying Delay Estimation

2021 ◽  
pp. 1-1
Author(s):  
Beth Jelfs ◽  
Shuai Sun ◽  
Kamran Ghorbani ◽  
Christopher Gilliam
Keyword(s):  
Delay Estimation ◽  
Time Varying ◽  
Pass Filter ◽  
Time Varying Delay ◽  
Varying Delay
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