DualStream Contextual Fusion Network: Efficient Target Speaker Extraction by Leveraging Mixture and Enrollment Interactions

Target speaker extraction focuses on extracting a target speech signal from an environment with multiple speakers by leveraging an enrollment. Existing methods predominantly rely on speaker embeddings obtained from the enrollment, potentially disregarding the contextual information and the internal interactions between the mixture and enrollment. In this paper, we propose a novel DualStream Contextual Fusion Network (DCF-Net) in the time-frequency (T-F) domain. Specifically, DualStream Fusion Block (DSFB) is introduced to obtain contextual information and capture the interactions between contextualized enrollment and mixture representation across both spatial and channel dimensions, and then rich and consistent representations are utilized to guide the extraction network for better extraction. Experimental results demonstrate that DCF-Net outperforms state-of-the-art (SOTA) methods, achieving a scale-invariant signal-to-distortion ratio improvement (SI-SDRi) of 21.6 dB on the benchmark dataset, and exhibits its robustness and effectiveness in both noise and reverberation scenarios. In addition, the wrong extraction results of our model, called target confusion problem, reduce to 0.4%, which highlights the potential of DCF-Net for practical applications.

Beamforming Design with Partial Channel Estimation and Feedback for FDD RIS-Assisted Systems

Beamforming design with partial channel estimation and feedback for frequency-division duplexing (FDD) reconfigurable intelligent surface (RIS) assisted systems is considered in this paper. We leverage the observation that path angle information (PAI) varies more slowly than path gain information (PGI). Then, several dominant paths are selected among all the cascaded paths according to the known PAI for maximizing the spectral efficiency of downlink data transmission. To acquire the dominating path gain information (DPGI, also regarded as the path gains of selected dominant paths) at the base station (BS), we propose a DPGI estimation and feedback scheme by jointly beamforming design at BS and RIS. Both the required number of downlink pilot signals and the length of uplink feedback vector are reduced to the number of dominant paths, and thus we achieve a great reduction of the pilot overhead and feedback overhead. Furthermore, we optimize the active BS beamformer and passive RIS beamformer by exploiting the feedback DPGI to further improve the spectral efficiency. From numerical results, we demonstrate the superiority of our proposed algorithms over the conventional schemes.