Bibliography: Feedback As Continuous Calibration

Mirrored copies live under research/feedback-calibration/mirrors/.

Echo / Feedback System Identification

  • S. Goetze, M. Kallinger, A. Mertins, and K. Kammeyer, “System Identification for Multi-Channel Listening-Room Compensation Using an Acoustic Echo Canceller,” HSCMA 2008.
    Mirror: mirrors/goetze-2008-aec-listening-room-compensation.html

  • F. Lindstrom, C. Schueldt, and I. Claesson, “Efficient Multichannel NLMS Implementation for Acoustic Echo Cancellation,” EURASIP Journal on Audio, Speech, and Music Processing, 2007.
    Mirror: mirrors/lindstrom-2007-efficient-multichannel-nlms.html

  • J.-M. Valin, “A New Robust Frequency Domain Echo Canceller With Closed-Loop Learning Rate Adaptation,” arXiv:1602.08609, 2016.
    Mirror: mirrors/valin-2016-frequency-domain-echo-canceller-learning-rate.pdf

  • “Double-Talk Robust Acoustic Echo Cancellation,” EUSIPCO 2021.
    Mirror: mirrors/eusipco-2021-double-talk-robust-aec.pdf

  • Apple Machine Learning Research, “Double-talk Robust Multichannel Acoustic Echo Cancellation Using Least Squares MIMO Adaptive Filtering,” 2020.
    Mirror: mirrors/apple-mimo-adaptive-echo-cancellation.html

Online Room / Acoustic Impulse Response Estimation

  • J. Nikunen and T. Virtanen, “Estimation of Time-Varying Room Impulse Responses of Multiple Sound Sources from Observed Mixture and Isolated Source Signals,” ICASSP 2018.
    Mirror: mirrors/nikunen-virtanen-2018-time-varying-rir.pdf

  • T. Haubner, A. Brendel, and W. Kellermann, “Online Acoustic System Identification Exploiting Kalman Filtering and an Adaptive Impulse Response Subspace Model,” arXiv:2105.03337, 2021.
    Mirror: mirrors/haubner-2021-online-acoustic-system-identification.pdf

Notes

MathWorks AEC and Filtered-X LMS examples were consulted via browser snippets/opened page content, but direct mirroring was blocked by access controls from PowerShell. They are not relied on as mirrored primary artifacts in the summary.