Selective-Memory Meta-Learning with Environment Representations for Sound Event Localization and Detection
CoRR(2023)
摘要
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.
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