Leveraging Sound Local and Global Features for Language-Queried Target Sound Extraction

Language-queried target sound extraction is a fundamental audio-language task that aims to estimate the audio signal of the target sound event class by a natural language expression in a sound mixture. One of the key challenges of this task is leveraging the language expression to highlight the target sound features in the noisy mixture interpretably. In this paper, we leverage language expression to guide the model to extract the most informative features of the target sound event by adaptively using local and global features, and we present a novel language-aware synergic attention network (LASA-Net) for language-queried target sound extraction, as the first attempt to leverage local and global operations using language representation to extract target sound in single or multiple sound source environments. In particular, language-aware synergic attention consists of a local operation submodule, a global operation submodule, and an interaction submodule, in which local and global operation submodules extract sound local and global features while the interaction submodule adaptively selects the most discriminative features with the guidance of linguistic features. In addition, we introduce a linguistic-acoustic fusion module that leverages the well-proven correlation modeling power of self-attention for excavating helpful multi-modal contexts. Extensive experiments demonstrate that our proposed LASA-Net is able to achieve state-of-the-art performance while maintaining an attractive computational complexity.