Speech separation has recently made significant progress thanks to the fine-grained vision used in time-domain methods. However, several studies have shown that adopting Short-Time Fourier Transform (STFT) for feature extraction could be beneficial when encountering harsher conditions, such as noise or reverberation. Therefore, we propose a magnitude-conditioned time-domain framework, ConSep, to inherit the beneficial characteristics. The experiment shows that ConSep promotes performance in anechoic, noisy, and reverberant settings compared to two celebrated methods, SepFormer and Bi-Sep. Furthermore, we visualize the components of ConSep to strengthen the advantages and cohere with the actualities we have found in preliminary studies.

中文翻译：

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ConSep：通过幅度调节实现噪声和混响鲁棒的语音分离框架

由于时域方法中使用的细粒度视觉，语音分离最近取得了重大进展。然而，多项研究表明，在遇到噪声或混响等更恶劣的条件时，采用短时傅立叶变换 (STFT) 进行特征提取可能会很有帮助。因此，我们提出了一种幅度条件时域框架 ConSep 来继承有益的特征。实验表明，与两种著名的方法 SepFormer 和 Bi-Sep 相比，ConSep 可以提高消声、噪声和混响环境中的性能。此外，我们将 ConSep 的组成部分可视化，以增强优势并与我们在初步研究中发现的现实相一致。