Identifiability Conditions for Acoustic Feedback Cancellation with the Two-Channel Adaptive Feedback Canceller Algorithm

TL;DR

This paper establishes new identifiability conditions for acoustic feedback cancellation using the two-channel adaptive feedback canceller, extending previous delay-based criteria to an invertibility-based framework and proposing a measure for monitoring identifiability.

Contribution

It generalizes the feedback path identifiability conditions for the 2ch-AFC algorithm from delay-based to invertibility-based criteria, enhancing understanding of system stability.

Findings

Identifiability can be achieved when the forward path order exceeds the AR model order.

The correlation matrix inversion condition number can monitor identifiability.

The new conditions extend previous delay-based criteria to a broader invertibility framework.

Abstract

In audio signal processing applications with a microphone and a loudspeaker within the same acoustic environment, the loudspeaker signals can feed back into the microphone, thereby creating a closed-loop system that potentially leads to system instability. To remove this acoustic coupling, prediction error method (PEM) feedback cancellation algorithms aim to identify the feedback path between the loudspeaker and the microphone by assuming that the input signal can be modelled by means of an autoregressive (AR) model. It has previously been shown that this PEM framework and resulting algorithms can identify the feedback path correctly in cases where the forward path from microphone to loudspeaker is sufficiently time-varying or non-linear, or when the forward path delay equals or exceeds the order of the AR model. In this paper, it is shown that this delay-based condition can be generalised for one particular PEM-based algorithm, the so-called two-channel adaptive feedback canceller (2ch-AFC), to an invertibility-based condition, for which it is shown that identifiability can be achieved when the order of the forward path feedforward filter exceeds the order of the AR model. Additionally, the condition number of inversion of the correlation matrix as used in the 2ch-AFC algorithm can serve as a measure for monitoring the identifiability.