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📄 Abstract
Abstract: Fixed beamforming is widely used in practice since it does not depend on the
estimation of noise statistics and provides relatively stable performance.
However, a single beamformer cannot adapt to varying acoustic conditions, which
limits its interference suppression capability. To address this, adaptive
convex combination (ACC) algorithms have been introduced, where the outputs of
multiple fixed beamformers are linearly combined to improve robustness.
Nevertheless, ACC often fails in highly non-stationary scenarios, such as
rapidly moving interference, since its adaptive updates cannot reliably track
rapid changes. To overcome this limitation, we propose a frame-online neural
fusion framework for multiple distortionless differential beamformers, which
estimates the combination weights through a neural network. Compared with
conventional ACC, the proposed method adapts more effectively to dynamic
acoustic environments, achieving stronger interference suppression while
maintaining the distortionless constraint.
Authors (7)
Yuanhang Qian
Kunlong Zhao
Jilu Jin
Xueqin Luo
Gongping Huang
Jingdong Chen
+1 more
Submitted
October 28, 2025
Key Contributions
Proposes a frame-online neural fusion framework for multiple distortionless differential beamformers. This method estimates combination weights via a neural network, enabling more effective adaptation to dynamic acoustic environments and stronger interference suppression compared to conventional ACC algorithms.
Business Value
Improved clarity and intelligibility of speech in noisy or dynamic environments, leading to better user experiences in voice assistants, teleconferencing, and hearing aids.