This paper tackles the scarcity of benchmarking data in disentangled auditory
representation learning. We introduce SynTone, a synthetic dataset with
explicit ground truth explanatory factors for evaluating disentanglement
techniques. Benchmarking state-of-the-art methods on SynTone highlights its
utility for method evaluation. Our results underscore strengths and limitations
in audio disentanglement, motivating future research.

本文解决了解缠音频表示学习中基准数据的稀缺问题。我们介绍了 SynTone，这是一个合成数据集，具有明确的基准解释因素，用于评估解缠技术。在 SynTone 上评估最先进的方法展示了它在方法评估中的实用性。我们的结果强调了音频解缠的优势和局限性，推动了未来的研究。

通过受控合成学习解耦的音频表征

Learning Disentangled Audio Representations through Controlled Synthesis

Contrastive learning has recently emerged as a promising approach for
learning data representations that discover and disentangle the explanatory
factors of the data. Previous analyses of such approaches have largely focused
on individual contrastive losses, such as noise-contrastive estimation (NCE)
and InfoNCE, and rely on specific assumptions about the data generating
process. This paper extends the theoretical guarantees for disentanglement to a
broader family of contrastive methods, while also relaxing the assumptions
about the data distribution. Specifically, we prove identifiability of the true
latents for four contrastive losses studied in this paper, without imposing
common independence assumptions. The theoretical findings are validated on
several benchmark datasets. Finally, practical limitations of these methods are
also investigated.

通过对多种对比方法的理论分析，本论文扩展了有关数据表达的对比学习的理论保证，并验证了这些发现在多个基准数据集上的实际有效性。

迈向一个统一的对比学习框架以实现解耦表征

Towards a Unified Framework of Contrastive Learning for Disentangled  Representations

Necessity and sufficiency are the building blocks of all successful
explanations. Yet despite their importance, these notions have been
conceptually underdeveloped and inconsistently applied in explainable
artificial intelligence (XAI), a fast-growing research area that is so far
lacking in firm theoretical foundations. Building on work in logic,
probability, and causality, we establish the central role of necessity and
sufficiency in XAI, unifying seemingly disparate methods in a single formal
framework. We provide a sound and complete algorithm for computing explanatory
factors with respect to a given context, and demonstrate its flexibility and
competitive performance against state of the art alternatives on various tasks.

在解释人工智能（XAI）这一快速发展的研究领域中，我们建立起必要性和充分性在 XAI 中的中心作用，通过逻辑、概率和因果关系的工作，将看似分散的方法统一到一个单一的形式框架中，并提供了一个可靠、完整的算法来计算与给定上下文相关的解释因素，展示了其灵活性和在各种任务上与最先进的替代方案的可比性。

必要性和充分性引起的本地解释：统一理论和实践

Local Explanations via Necessity and Sufficiency: Unifying Theory and  Practice

It has been postulated that a good representation is one that disentangles
the underlying explanatory factors of variation. However, it remains an open
question what kind of training framework could potentially achieve that.
Whereas most previous work focuses on the static setting (e.g., with images),
we postulate that some of the causal factors could be discovered if the learner
is allowed to interact with its environment. The agent can experiment with
different actions and observe their effects. More specifically, we hypothesize
that some of these factors correspond to aspects of the environment which are
independently controllable, i.e., that there exists a policy and a learnable
feature for each such aspect of the environment, such that this policy can
yield changes in that feature with minimal changes to other features that
explain the statistical variations in the observed data. We propose a specific
objective function to find such factors, and verify experimentally that it can
indeed disentangle independently controllable aspects of the environment
without any extrinsic reward signal.

通过探究代理与环境的交互作用，并提出一种特定的目标函数，本文证实无需外在激励信号的情况下，能够发现独立可控因素并实现解耦。