用神经网络在线融合多个无失真波束成形器，提升语音增强在动态环境下的抗干扰能力。
Online neural fusion of distortionless differential beamformers for robust speech enhancement
- 通过神经网络实时估计多波束输出的融合权重
- 在快速变化声学环境下显著降低干扰，保持语音无失真
- 适合高动态噪声场景下的实时语音增强应用
固定波束成形因不依赖噪声统计估计且性能稳定，被广泛应用于实际。然而单一波束成形无法适应变化的声学环境，限制了其干扰抑制能力。为此，自适应凸组合（ACC）算法被提出，通过线性组合多个固定波束成形器的输出以提高鲁棒性。但ACC在高度非平稳场景（如快速移动的干扰源）中表现不佳，因其自适应更新难以可靠跟踪快速变化。为此，本文提出一种帧级在线神经融合框架，用于多个无失真差分波束成形器的融合，通过神经网络估计组合权重。相比传统ACC，该方法在动态声学环境中具备更强的适应能力，实现更优的干扰抑制效果，同时保持无失真约束。
原文摘要 · Abstract (English)
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.
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