通过序列建模的输入自适应频谱特征压缩用于声源分离
Input-Adaptive Spectral Feature Compression by Sequence Modeling for Source Separation
AI总结：
本文提出SFC方法，通过序列建模实现输入自适应的频谱特征压缩，提升声源分离性能。
摘要：
时频域双路径模型已显示出强大的性能，并广泛应用于声源分离。由于其计算成本随着频谱分量数量的增长而增加，这些模型在高采样率任务如音乐声源分离（MSS）和影视音频声源分离（CASS）中通常使用带分割（BS）模块。BS编码器通过为每个预定义的子带编码特征来压缩频率信息。它通过引入一种诱导偏差，更强调低频部分，从而实现有效的压缩。尽管其成功，BS模块有两个固有局限性：（i）它不是输入自适应的，防止使用输入依赖的信息；（ii）参数数量较大，因为每个子带都需要专用模块。为了解决这些问题，我们提出了频谱特征压缩（SFC）。SFC通过单个序列建模模块压缩输入，使其既输入自适应又参数高效。我们研究了SFC的两个变种，一个是基于交叉注意力，另一个是基于Mamba，并引入受BS模块启发的诱导偏差，使它们适合频率信息压缩。在MSS和CASS任务中的实验表明，SFC模块在不同分离器大小和压缩比下均优于BS模块。我们还提供了一项分析，显示SFC能够自适应地从输入中捕获频率模式。
英文摘要：
Time-frequency domain dual-path models have demonstrated strong performance and are widely used in source separation. Because their computational cost grows with the number of frequency bins, these models often use the band-split (BS) module in high-sampling-rate tasks such as music source separation (MSS) and cinematic audio source separation (CASS). The BS encoder compresses frequency information by encoding features for each predefined subband. It achieves effective compression by introducing an inductive bias that places greater emphasis on low-frequency parts. Despite its success, the BS module has two inherent limitations: (i) it is not input-adaptive, preventing the use of input-dependent information, and (ii) the parameter count is large, since each subband requires a dedicated module. To address these issues, we propose Spectral Feature Compression (SFC). SFC compresses the input using a single sequence modeling module, making it both input-adaptive and parameter-efficient. We investigate two variants of SFC, one based on cross-attention and the other on Mamba, and introduce inductive biases inspired by the BS module to make them suitable for frequency information compression. Experiments on MSS and CASS tasks demonstrate that the SFC module consistently outperforms the BS module across different separator sizes and compression ratios. We also provide an analysis showing that SFC adaptively captures frequency patterns from the input.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
