In this paper, we introduce a novel training framework designed to comprehensively address the acoustic howling issue by examining its fundamental formation process. This framework integrates a neural network (NN) module into the closed-loop system during training with signals generated recursively on the fly to closely mimic the streaming process of acoustic howling suppression (AHS). The proposed recursive training strategy bridges the gap between training and real-world inference scenarios, marking a departure from previous NN-based methods that typically approach AHS as either noise suppression or acoustic echo cancellation. Within this framework, we explore two methodologies: one exclusively relying on NN and the other combining NN with the traditional Kalman filter. Additionally, we propose strategies, including howling detection and initialization using pre-trained offline models, to bolster trainability and expedite the training process. Experimental results validate that this framework offers a substantial improvement over previous methodologies for acoustic howling suppression.

翻译：本文提出了一种新颖的训练框架，旨在通过深入分析声学啸叫的基本形成过程来全面解决该问题。该框架在训练过程中将神经网络模块集成到闭环系统中，并利用实时递归生成的信号，紧密模拟声学啸叫抑制的流式处理过程。所提出的递归训练策略弥合了训练场景与实际推理场景之间的差距，这与以往将声学啸叫抑制视为噪声抑制或声学回声消除的基于神经网络的方法有本质区别。在该框架下，我们探索了两种方法：一种完全依赖神经网络，另一种则将神经网络与传统卡尔曼滤波器相结合。此外，我们提出了包括利用预训练离线模型进行啸叫检测和初始化的策略，以增强可训练性并加速训练过程。实验结果验证了该框架在声学啸叫抑制方面相比以往方法具有显著优势。