A General Close-loop Predictive Coding Framework for Auditory Working Memory

• URL: http://arxiv.org/abs/2503.12506v1
• Date: Sun, 16 Mar 2025 13:57:37 GMT
• Title: A General Close-loop Predictive Coding Framework for Auditory Working Memory
• Authors: Zhongju Yuan, Geraint Wiggins, Dick Botteldooren,
• Abstract summary: We propose a general framework based on a close-loop predictive coding paradigm to perform short auditory signal memory tasks.<n>The framework is evaluated on two widely used benchmark datasets for environmental sound and speech.
• Score: 4.7368661961661775
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• On Memory Construction and Retrieval for Personalized Conversational Agents [69.46887405020186]
  We propose SeCom, a method that constructs the memory bank at segment level by introducing a conversation segmentation model.<n> Experimental results show that SeCom exhibits a significant performance advantage over baselines on long-term conversation benchmarks LOCOMO and Long-MT-Bench+.
  arXiv  Detail & Related papers  (2025-02-08T14:28:36Z)
• InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions [104.90258030688256]
  This project introduces disentangled streaming perception, reasoning, and memory mechanisms, enabling real-time interaction with streaming video and audio input. This project simulates human-like cognition, enabling multimodal large language models to provide continuous and adaptive service over time.
  arXiv  Detail & Related papers  (2024-12-12T18:58:30Z)
• Hierarchical Working Memory and a New Magic Number [1.024113475677323]
  We propose a recurrent neural network model for chunking within the framework of the synaptic theory of working memory. Our work provides a novel conceptual and analytical framework for understanding the on-the-fly organization of information in the brain that is crucial for cognition.
  arXiv  Detail & Related papers  (2024-08-14T16:03:47Z)
• Analysis of the Memorization and Generalization Capabilities of AI Agents: Are Continual Learners Robust? [91.682459306359]
  In continual learning (CL), an AI agent learns from non-stationary data streams under dynamic environments. In this paper, a novel CL framework is proposed to achieve robust generalization to dynamic environments while retaining past knowledge. The generalization and memorization performance of the proposed framework are theoretically analyzed.
  arXiv  Detail & Related papers  (2023-09-18T21:00:01Z)
• Pin the Memory: Learning to Generalize Semantic Segmentation [68.367763672095]
  We present a novel memory-guided domain generalization method for semantic segmentation based on meta-learning framework. Our method abstracts the conceptual knowledge of semantic classes into categorical memory which is constant beyond the domains.
  arXiv  Detail & Related papers  (2022-04-07T17:34:01Z)
• Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video [53.14186293442669]
  We identify two important clues for surgical instrument perception, including local temporal dependency from adjacent frames and global semantic correlation in long-range duration. We propose a novel dual-memory network (DMNet) to relate both global and local-temporal knowledge. Our method largely outperforms the state-of-the-art works on segmentation accuracy while maintaining a real-time speed.
  arXiv  Detail & Related papers  (2021-09-28T10:10:14Z)
• Temporal Memory Relation Network for Workflow Recognition from Surgical Video [53.20825496640025]
  We propose a novel end-to-end temporal memory relation network (TMNet) for relating long-range and multi-scale temporal patterns. We have extensively validated our approach on two benchmark surgical video datasets.
  arXiv  Detail & Related papers  (2021-03-30T13:20:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.