用闭环预测编码框架模拟听觉工作记忆，提升神经网络对声音信息的短期存储能力。
A General Close-loop Predictive Coding Framework for Auditory Working Memory
- 基于闭环预测编码机制建模听觉工作记忆
- 在环境音和语音数据集上均实现高语义相似度
- 适用于语音处理与认知神经科学交叉研究
听觉工作记忆对语言习得、对话等日常活动至关重要，涉及对已不存在于环境中的信息进行临时存储与操作。尽管在神经科学和认知科学中已有广泛研究，但其在神经网络中的建模仍较有限。为弥补这一空白，我们提出一种基于闭环预测编码范式的通用框架，用于完成短时听觉信号记忆任务。该框架在两个广泛使用的环境音和语音基准数据集上进行了评估，结果显示在两个数据集上均表现出高语义相似性。
原文摘要 · Abstract (English)
Auditory working memory is essential for various daily activities, such as language acquisition, conversation. It involves the temporary storage and manipulation of information that is no longer present in the environment. While extensively studied in neuroscience and cognitive science, research on its modeling within neural networks remains limited. To address this gap, we propose a general framework based on a close-loop predictive coding paradigm to perform short auditory signal memory tasks. The framework is evaluated on two widely used benchmark datasets for environmental sound and speech, demonstrating high semantic similarity across both datasets.
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