Coarse-to-Fine Target Speaker Extraction Based on Contextual Information Exploitation

To address the cocktail party problem, the target speaker extraction (TSE) has received increasing attention recently. Typically, the TSE is explored in two scenarios. The first scenario is a specific one, where the target speaker is present and the signal received by the microphone contains at least two speakers. The second scenario is a universal one, where the target speaker may be present or absent and the received signal may contain one or multiple speakers. Numerous TSE studies utilize the target speaker's embedding to guide the extraction. However, solely utilizing this embedding may not fully leverage the contextual information within the enrollment. To address this limitation, a novel approach that directly exploits the contextual information in the time-frequency (T-F) domain was proposed. This paper improves this approach by integrating our previously proposed coarse-to-fine framework. For the specific scenario, an interaction block is employed to facilitate direct interaction between the T-F representations of the enrollment and received signal. This direct interaction leads to the consistent representation of the enrollment that serves as guidance for the coarse extraction. Afterwards, the T-F representation of the coarsely extracted signal is utilized to guide the refining extraction. The residual representation obtained during the refining extraction increases the extraction precision. Besides, this paper explores an undisturbed universal scenario where the noise and reverberation are not considered. A two-level decision-making scheme is devised to generalize our proposed method for this undisturbed universal scenario. The proposed method achieves high performance and is proven effective for both scenarios.