4 research outputs found
improving partition-block-based acoustic echo canceler in under-modeling scenarios
Recently, a partitioned-block-based frequency-domain Kalman filter (PFKF) has
been proposed for acoustic echo cancellation. Compared with the normal
frequency-domain Kalman filter, the PFKF utilizes the partitioned-block
structure, resulting in both fast convergence and low time-latency. We present
an analysis of the steady-state behavior of the PFKF and found that it suffers
from a biased steady-state solution when the filter is of deficient length.
Accordingly, we propose an effective modification that has the benefit of the
guaranteed optimal steady-state behavior. Simulations are conducted to validate
the improved performance of the proposed method.Comment: accepted by interspeech202
Efficient improvement of frequency-domain Kalman filter
The frequency-domain Kalman filter (FKF) has been utilized in many audio
signal processing applications due to its fast convergence speed and
robustness. However, the performance of the FKF in under-modeling situations
has not been investigated. This paper presents an analysis of the steady-state
behavior of the commonly used diagonalized FKF and reveals that it suffers from
a biased solution in under-modeling scenarios. Two efficient improvements of
the FKF are proposed, both having the benefits of the guaranteed optimal
steady-state behavior at the cost of a very limited increase of the
computational burden. The convergence behavior of the proposed algorithms is
also compared analytically. Computer simulations are conducted to validate the
improved performance of the proposed methods.Comment: 5 pages, 3 figure
Nonlinear Residual Echo Suppression Based on Multi-stream Conv-TasNet
Acoustic echo cannot be entirely removed by linear adaptive filters due to
the nonlinear relationship between the echo and far-end signal. Usually a post
processing module is required to further suppress the echo. In this paper, we
propose a residual echo suppression method based on the modification of fully
convolutional time-domain audio separation network (Conv-TasNet). Both the
residual signal of the linear acoustic echo cancellation system, and the output
of the adaptive filter are adopted to form multiple streams for the
Conv-TasNet, resulting in more effective echo suppression while keeping a lower
latency of the whole system. Simulation results validate the efficacy of the
proposed method in both single-talk and double-talk situations.Comment: 5 pages, 3 figure
A Synergistic Kalman- and Deep Postfiltering Approach to Acoustic Echo Cancellation
We introduce a synergistic approach to double-talk robust acoustic echo
cancellation combining adaptive Kalman filtering with a deep neural
network-based postfilter. The proposed algorithm overcomes the well-known
limitations of Kalman filter-based adaptation control in scenarios
characterized by abrupt echo path changes. As the key innovation, we suggest to
exploit the different statistical properties of the interfering signal
components for robustly estimating the adaptation step size. This is achieved
by leveraging the postfilter near-end estimate and the estimation error of the
Kalman filter. The proposed synergistic scheme allows for rapid reconvergence
of the adaptive filter after abrupt echo path changes without compromising the
steady state performance achieved by state-of-the-art approaches in static
scenarios