用奖励驱动探索，让模型主动发现新声音源。
A conceptual framework for learning to listen by reward: Curiosity-driven search for novel sources
- 通过持续寻找新声源实现奖励驱动的听觉学习。
- 首次概念验证展示该方法可行性。
- 适合研究自监督音频感知与好奇心驱动学习的学者。
强化学习是一种强大的学习范式，在多个领域推动了进展，其核心优势在于通过高层目标学习而无需细粒度标注。然而在音频领域，其应用远不及计算机视觉等方向。关键问题在于：智能体如何仅通过奖励驱动的探索来学会聆听？本文综述了前期尝试，并提出一种新的奖励驱动听觉学习概念框架。该方法依赖于持续搜索新颖的声音来源，构建了理论框架，讨论了关键技术挑战，并实现了首个概念验证，证明了该思路的可行性。
原文摘要 · Abstract (English)
Reinforcement learning is a powerful learning paradigm that has spearheaded progress in numerous domains. Its core promise lies in learning through high-level goals without the need for granular labels. However, it still remains elusive in the realm of audio, where it has received substantially less attention than in computer vision or other domains. The key question remains: how can agents learn to listen purely via reward-driven exploration? In this contribution, we present an overview of previous attempts and a new conceptual framework for learning to listen by reward. Our approach depends on the continuous search for novel sound sources. We formulate our framework, discuss open technical challenges, and present a first proof-of-concept implementation that showcases the feasibility of our approach.
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