Environment shifts and conflicts present significant challenges for learning-based sound event localization and detection (SELD) methods. SELD systems, when trained in particular acoustic settings, often show restricted generalization capabilities for diverse acoustic environments. Furthermore, it is notably costly to obtain annotated samples for spatial sound events. Deploying a SELD system in a new environment requires extensive time for re-training and fine-tuning. To overcome these challenges, we propose environment-adaptive Meta-SELD, designed for efficient adaptation to new environments using minimal data. Our method specifically utilizes computationally synthesized spatial data and employs Model-Agnostic Meta-Learning (MAML) on a pre-trained, environment-independent model. The method then utilizes fast adaptation to unseen real-world environments using limited samples from the respective environments. Inspired by the Learning-to-Forget approach, we introduce the concept of selective memory as a strategy for resolving conflicts across environments. This approach involves selectively memorizing target-environment-relevant information and adapting to the new environments through the selective attenuation of model parameters. In addition, we introduce environment representations to characterize different acoustic settings, enhancing the adaptability of our attenuation approach to various environments. We evaluate our proposed method on the development set of the Sony-TAU Realistic Spatial Soundscapes 2023 (STARSS23) dataset and computationally synthesized scenes. Experimental results demonstrate the superior performance of the proposed method compared to conventional supervised learning methods, particularly in localization.

翻译：环境变化与冲突对基于学习的声音事件定位与检测（SELD）方法构成了显著挑战。当SELD系统在特定声学环境中训练时，往往对不同声学环境的泛化能力受限。此外，获取空间声音事件的标注样本成本高昂。将SELD系统部署到新环境需要大量时间进行重新训练和微调。为克服这些挑战，我们提出环境自适应元学习SELD方法（Meta-SELD），旨在利用最少数据高效适应新环境。该方法利用计算合成的空间数据，在预训练的环境无关模型上采用模型无关元学习（MAML），随后利用各环境中的少量样本快速适应未见过的真实场景。受"学会遗忘"方法启发，我们引入选择性记忆概念作为跨环境冲突解决策略：通过选择性衰减模型参数，仅记忆目标环境相关信息并适应新环境。同时，提出环境表征方法刻画不同声学设置，增强参数衰减策略对各种环境的适应性。我们在Sony-TAU真实空间声景2023（STARSS23）数据集开发集及计算合成场景上评估所提方法。实验结果表明，相较于传统监督学习方法，本方法在定位性能上表现出显著优势。