One key aspect differentiating data-driven single- and multi-channel speech enhancement and dereverberation methods is that both the problem formulation and complexity of the solutions are considerably more challenging in the latter case. Additionally, with limited computational resources, it is cumbersome to train models that require the management of larger datasets or those with more complex designs. In this scenario, an unverified hypothesis that single-channel methods can be adapted to multi-channel scenarios simply by processing each channel independently holds significant implications, boosting compatibility between sound scene capture and system input-output formats, while also allowing modern research to focus on other challenging aspects, such as full-bandwidth audio enhancement, competitive noise suppression, and unsupervised learning. This study verifies this hypothesis by comparing the enhancement promoted by a basic single-channel speech enhancement and dereverberation model with two other multi-channel models tailored to separate clean speech from noisy 3D mixes. A direction of arrival estimation model was used to objectively evaluate its capacity to preserve spatial information by comparing the output signals with ground-truth coordinate values. Consequently, a trade-off arises between preserving spatial information with a more straightforward single-channel solution at the cost of obtaining lower gains in intelligibility scores.

翻译：区分数据驱动单通道与多通道语音增强及去混响方法的一个关键方面在于，后者的问题表述和解决方案复杂度均显著更高。此外，在计算资源有限的情况下，训练需要管理更大规模数据集或采用更复杂设计的模型较为困难。在此背景下，一个未经证实的假设——即单通道方法可通过独立处理各声道直接适配至多通道场景——具有重要影响，既能提升声音场景采集与系统输入输出格式之间的兼容性，又能使现代研究聚焦于其他挑战性方向（如全频带音频增强、竞争性噪声抑制及无监督学习）。本研究通过对比基础单通道语音增强与去混响模型与两种专门用于从含噪3D混合信号中分离纯净语音的多通道模型的性能提升效果，验证了这一假设。通过使用到达方向估计模型对比输出信号与真实坐标值，客观评估其保持空间信息的能力。结果表明，采用更直接的单通道解决方案可在牺牲部分语音清晰度分数提升的代价下，更好地保留空间信息。