2 research outputs found
Deep model with built-in cross-attention alignment for acoustic echo cancellation
With recent research advances, deep learning models have become an attractive
choice for acoustic echo cancellation (AEC) in real-time teleconferencing
applications. Since acoustic echo is one of the major sources of poor audio
quality, a wide variety of deep models have been proposed. However, an
important but often omitted requirement for good echo cancellation quality is
the synchronization of the microphone and far end signals. Typically
implemented using classical algorithms based on cross-correlation, the
alignment module is a separate functional block with known design limitations.
In our work we propose a deep learning architecture with built-in
self-attention based alignment, which is able to handle unaligned inputs,
improving echo cancellation performance while simplifying the communication
pipeline. Moreover, we show that our approach achieves significant improvements
for difficult delay estimation cases on real recordings from AEC Challenge data
set
ICASSP 2023 Acoustic Echo Cancellation Challenge
The ICASSP 2023 Acoustic Echo Cancellation Challenge is intended to stimulate
research in acoustic echo cancellation (AEC), which is an important area of
speech enhancement and is still a top issue in audio communication. This is the
fourth AEC challenge and it is enhanced by adding a second track for
personalized acoustic echo cancellation, reducing the algorithmic + buffering
latency to 20ms, as well as including a full-band version of AECMOS. We open
source two large datasets to train AEC models under both single talk and double
talk scenarios. These datasets consist of recordings from more than 10,000 real
audio devices and human speakers in real environments, as well as a synthetic
dataset. We open source an online subjective test framework and provide an
objective metric for researchers to quickly test their results. The winners of
this challenge were selected based on the average mean opinion score (MOS)
achieved across all scenarios and the word accuracy (WAcc) rate.Comment: arXiv admin note: substantial text overlap with arXiv:2202.13290,
arXiv:2009.0497