We propose a self-supervised approach for learning to perform audio source separation in videos based on natural language queries, using only unlabeled video and audio pairs as training data. A key challenge in this task is learning to associate the linguistic description of a sound-emitting object to its visual features and the corresponding components of the audio waveform, all without access to annotations during training. To overcome this challenge, we adapt off-the-shelf vision-language foundation models to provide pseudo-target supervision via two novel loss functions and encourage a stronger alignment between the audio, visual and natural language modalities. During inference, our approach can separate sounds given text, video and audio input, or given text and audio input alone. We demonstrate the effectiveness of our self-supervised approach on three audio-visual separation datasets, including MUSIC, SOLOS and AudioSet, where we outperform state-of-the-art strongly supervised approaches despite not using object detectors or text labels during training.

翻译：我们提出一种基于自然语言查询的音频源分离自监督学习方法，仅使用未标注的视频-音频对作为训练数据。该任务的核心挑战在于：在训练过程中完全无标注的情况下，学习建立发声实体的语言描述与其视觉特征及音频波形对应成分之间的关联。为克服这一挑战，我们通过两种新颖的损失函数，利用现成的视觉-语言基础模型提供伪目标监督，并增强音频、视觉和自然语言模态之间的对齐程度。在推理阶段，我们的方法既可根据文本、视频和音频输入分离声音，也可仅凭文本和音频输入实现分离。我们在包括MUSIC、SOLOS和AudioSet在内的三个音频-视觉分离数据集上验证了该自监督方法的有效性。尽管训练中未使用目标检测器或文本标签，我们的方法仍优于当前最先进的强监督方法。