@InProceedings{8902963,
  author = {F. Yang and G. Enzner and J. Yang},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Transient Analysis of Partitioned-Block Frequency-Domain Adaptive Filters},
  year = {2019},
  pages = {1-5},
  abstract = {The frequency-domain adaptive filter (FDAF) is very useful for instance acoustic signal processing. The partitioned-block FDAF (PBFDAF) is a generalization of the FDAF and becomes more popular due to its low latency. Some efforts have been done toward the convergence analysis of PBFDAFs, but they usually use strong approximations and hence came to inaccurate results. This paper presents a unified approach to the transient analysis of both the constrained and unconstrained PBFDAFs based on the overlap-save structure. Using the independence assumption, we derive the analytical expressions for the mean and mean-square performance of PBFDAFs. Our analysis does not assume a specific model for the inputs and provides a quite general framework. Computer simulations confirm a good match between our theory and experimental results.},
  keywords = {acoustic signal processing;adaptive filters;adaptive signal processing;echo suppression;filtering theory;frequency-domain analysis;least mean squares methods;transient analysis;transient analysis;constrained PBFDAFs;unconstrained PBFDAFs;partitioned-block frequency-domain adaptive filters;frequency-domain adaptive filter;instance acoustic signal processing;partitioned-block FDAF;PBFDAF;convergence analysis;strong approximations;Frequency-domain analysis;Transient analysis;Convergence;Signal processing algorithms;Analytical models;Covariance matrices;Europe;Adaptive filtering;frequency domain;convergence analysis;transient behavior},
  doi = {10.23919/EUSIPCO.2019.8902963},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570530181.pdf},
}

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Some efforts have been done toward the convergence analysis of PBFDAFs, but they usually use strong approximations and hence came to inaccurate results. This paper presents a unified approach to the transient analysis of both the constrained and unconstrained PBFDAFs based on the overlap-save structure. Using the independence assumption, we derive the analytical expressions for the mean and mean-square performance of PBFDAFs. Our analysis does not assume a specific model for the inputs and provides a quite general framework. Computer simulations confirm a good match between our theory and experimental results.","keywords":"acoustic signal processing;adaptive filters;adaptive signal processing;echo suppression;filtering theory;frequency-domain analysis;least mean squares methods;transient analysis;transient analysis;constrained PBFDAFs;unconstrained PBFDAFs;partitioned-block frequency-domain adaptive filters;frequency-domain adaptive filter;instance acoustic signal processing;partitioned-block FDAF;PBFDAF;convergence analysis;strong approximations;Frequency-domain analysis;Transient analysis;Convergence;Signal processing algorithms;Analytical models;Covariance matrices;Europe;Adaptive filtering;frequency domain;convergence analysis;transient behavior","doi":"10.23919/EUSIPCO.2019.8902963","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570530181.pdf","bibtex":"@InProceedings{8902963,\n author = {F. Yang and G. Enzner and J. Yang},\n booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},\n title = {Transient Analysis of Partitioned-Block Frequency-Domain Adaptive Filters},\n year = {2019},\n pages = {1-5},\n abstract = {The frequency-domain adaptive filter (FDAF) is very useful for instance acoustic signal processing. The partitioned-block FDAF (PBFDAF) is a generalization of the FDAF and becomes more popular due to its low latency. Some efforts have been done toward the convergence analysis of PBFDAFs, but they usually use strong approximations and hence came to inaccurate results. This paper presents a unified approach to the transient analysis of both the constrained and unconstrained PBFDAFs based on the overlap-save structure. Using the independence assumption, we derive the analytical expressions for the mean and mean-square performance of PBFDAFs. Our analysis does not assume a specific model for the inputs and provides a quite general framework. Computer simulations confirm a good match between our theory and experimental results.},\n keywords = {acoustic signal processing;adaptive filters;adaptive signal processing;echo suppression;filtering theory;frequency-domain analysis;least mean squares methods;transient analysis;transient analysis;constrained PBFDAFs;unconstrained PBFDAFs;partitioned-block frequency-domain adaptive filters;frequency-domain adaptive filter;instance acoustic signal processing;partitioned-block FDAF;PBFDAF;convergence analysis;strong approximations;Frequency-domain analysis;Transient analysis;Convergence;Signal processing algorithms;Analytical models;Covariance matrices;Europe;Adaptive filtering;frequency domain;convergence analysis;transient behavior},\n doi = {10.23919/EUSIPCO.2019.8902963},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570530181.pdf},\n}\n\n","author_short":["Yang, F.","Enzner, G.","Yang, J."],"key":"8902963","id":"8902963","bibbaseid":"yang-enzner-yang-transientanalysisofpartitionedblockfrequencydomainadaptivefilters-2019","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570530181.pdf"},"keyword":["acoustic signal processing;adaptive filters;adaptive signal processing;echo suppression;filtering theory;frequency-domain analysis;least mean squares methods;transient analysis;transient analysis;constrained PBFDAFs;unconstrained PBFDAFs;partitioned-block frequency-domain adaptive filters;frequency-domain adaptive filter;instance acoustic signal processing;partitioned-block FDAF;PBFDAF;convergence analysis;strong approximations;Frequency-domain analysis;Transient analysis;Convergence;Signal processing algorithms;Analytical models;Covariance matrices;Europe;Adaptive filtering;frequency domain;convergence analysis;transient behavior"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2019url.bib","creationDate":"2021-02-11T19:15:22.061Z","downloads":0,"keywords":["acoustic signal processing;adaptive filters;adaptive signal processing;echo suppression;filtering theory;frequency-domain analysis;least mean squares methods;transient analysis;transient analysis;constrained pbfdafs;unconstrained pbfdafs;partitioned-block frequency-domain adaptive filters;frequency-domain adaptive filter;instance acoustic signal processing;partitioned-block fdaf;pbfdaf;convergence analysis;strong approximations;frequency-domain analysis;transient analysis;convergence;signal processing algorithms;analytical models;covariance matrices;europe;adaptive filtering;frequency domain;convergence analysis;transient behavior"],"search_terms":["transient","analysis","partitioned","block","frequency","domain","adaptive","filters","yang","enzner","yang"],"title":"Transient Analysis of Partitioned-Block Frequency-Domain Adaptive Filters","year":2019,"dataSources":["NqWTiMfRR56v86wRs","r6oz3cMyC99QfiuHW"]}