A Time-domain Generalized Wiener Filter for Multi-channel Speech Separation

• URL: http://arxiv.org/abs/2112.03533v1
• Date: Tue, 7 Dec 2021 07:16:43 GMT
• Title: A Time-domain Generalized Wiener Filter for Multi-channel Speech Separation
• Authors: Yi Luo
• Abstract summary: Frequency-domain neural beamformers are the mainstream methods for recent multi-channel speech separation models. We propose a time-domain Wiener generalized filter (TD-GWF) as an extension to the conventional frequency-domain beamformers. Experiment results show that a significant performance improvement can be achieved by replacing frequency-domain beamformers by the TD-GWF.
• Score: 11.970226369922598
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Frequency-domain neural beamformers are the mainstream methods for recent multi-channel speech separation models. Despite their well-defined behaviors and the effectiveness, such frequency-domain beamformers still have the limitations of a bounded oracle performance and the difficulties of designing proper networks for the complex-valued operations. In this paper, we propose a time-domain generalized Wiener filter (TD-GWF), an extension to the conventional frequency-domain beamformers that has higher oracle performance and only involves real-valued operations. We also provide discussions on how TD-GWF can be connected to conventional frequency-domain beamformers. Experiment results show that a significant performance improvement can be achieved by replacing frequency-domain beamformers by the TD-GWF in the recently proposed sequential neural beamforming pipelines.

Related papers

• WaveRNet: Wavelet-Guided Frequency Learning for Multi-Source Domain-Generalized Retinal Vessel Segmentation [4.23704854635294]
  Domain-generalized retinal vessel segmentation is critical for automated ophthalmic diagnosis.<n>We propose WaveRNet, a wavelet-guided frequency learning framework for robust domain-generalized retinal vessel segmentation.
  arXiv  Detail & Related papers  (2026-01-09T16:58:29Z)
• WaveletDiff: Multilevel Wavelet Diffusion For Time Series Generation [15.28917997999402]
  WaveletDiff is a novel framework that trains diffusion models directly on wavelet coefficients to exploit the inherent multi-resolution structure of time series data.<n>It consistently outperforms state-of-the-art time-domain and frequency-domain generative methods on both short and long time series across five diverse performance metrics.
  arXiv  Detail & Related papers  (2025-10-13T18:47:33Z)
• Wavelet Fourier Diffuser: Frequency-Aware Diffusion Model for Reinforcement Learning [10.270002679268485]
  We propose a novel diffusion-based RL framework that integrates Discrete Wavelet Transform to decompose trajectories into low- and high-frequency components.<n>WFDiffuser effectively mitigates frequency shift, leading to smoother, more stable trajectories and improved decision-making performance.
  arXiv  Detail & Related papers  (2025-09-04T08:50:31Z)
• Wavelet-Guided Dual-Frequency Encoding for Remote Sensing Change Detection [67.84730634802204]
  Change detection in remote sensing imagery plays a vital role in various engineering applications, such as natural disaster monitoring, urban expansion tracking, and infrastructure management.<n>Most existing methods still rely on spatial-domain modeling, where the limited diversity of feature representations hinders the detection of subtle change regions.<n>We observe that frequency-domain feature modeling particularly in the wavelet domain amplify fine-grained differences in frequency components, enhancing the perception of edge changes that are challenging to capture in the spatial domain.
  arXiv  Detail & Related papers  (2025-08-07T11:14:16Z)
• SPJFNet: Self-Mining Prior-Guided Joint Frequency Enhancement for Ultra-Efficient Dark Image Restoration [3.2735437407166414]
  Current dark image restoration methods suffer from severe efficiency bottlenecks.<n>We propose an Efficient Self-Mining Prior-Guided Joint Frequency Enhancement Network (SPJFNet)
  arXiv  Detail & Related papers  (2025-08-06T03:06:29Z)
• Efficient Dual-domain Image Dehazing with Haze Prior Perception [17.18810808188725]
  Transformer-based models exhibit strong global modeling capabilities in single-image dehazing, but their high computational cost limits real-time applicability.<n>We propose the Dark Channel Guided Frequency-aware Dehazing Network (DGFDNet), a novel dual-domain framework that performs physically guided degradation alignment.<n>Experiments on four benchmark haze datasets demonstrate that DGFDNet achieves state-of-the-art performance with superior robustness and real-time efficiency.
  arXiv  Detail & Related papers  (2025-07-15T06:56:56Z)
• FAD: Frequency Adaptation and Diversion for Cross-domain Few-shot Learning [35.40065954148091]
  Cross-domain few-shot learning requires models to generalize from limited labeled samples under significant distribution shifts.<n>We introduce Frequency Adaptation and Diversion (FAD), a frequency-aware framework that explicitly models and modulates spectral components.<n>FAD consistently outperforms state-of-the-art methods on both seen and unseen domains.
  arXiv  Detail & Related papers  (2025-05-13T08:48:06Z)
• Integrating Frequency Guidance into Multi-source Domain Generalization for Bearing Fault Diagnosis [24.85752780864944]
  We propose the Fourier-based Augmentation Reconstruction Network, namely FARNet. The network comprises an amplitude spectrum sub-network and a phase spectrum sub-network, sequentially reducing the discrepancy between the source and target domains. To refine the decision boundary of our model output compared to conventional triplet loss, we propose a manifold triplet loss to contribute to generalization.
  arXiv  Detail & Related papers  (2025-02-01T20:23:03Z)
• Multi-scale Generative Modeling for Fast Sampling [38.570968785490514]
  In the wavelet domain, we encounter unique challenges, especially the sparse representation of high-frequency coefficients. We propose a multi-scale generative modeling in the wavelet domain that employs distinct strategies for handling low and high-frequency bands. As supported by the theoretical analysis and experimental results, our model significantly improve performance and reduce the number of trainable parameters, sampling steps, and time.
  arXiv  Detail & Related papers  (2024-11-14T11:01:45Z)
• ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series Forecasting [7.694820760102176]
  ATFNet is an innovative framework that combines a time domain module and a frequency domain module. We introduce Dominant Harmonic Series Energy Weighting, a novel mechanism for adjusting the weights between the two modules. Our Complex-valued Spectrum Attention mechanism offers a novel approach to discern the intricate relationships between different frequency combinations.
  arXiv  Detail & Related papers  (2024-04-08T04:41:39Z)
• WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting [61.64303388738395]
  We propose a Wavelet-Fourier Transform Network (WFTNet) for long-term time series forecasting. Tests on various time series datasets show WFTNet consistently outperforms other state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-09-20T13:44:18Z)
• Adaptive Frequency Filters As Efficient Global Token Mixers [100.27957692579892]
  We show that adaptive frequency filters can serve as efficient global token mixers. We take AFF token mixers as primary neural operators to build a lightweight neural network, dubbed AFFNet.
  arXiv  Detail & Related papers  (2023-07-26T07:42:28Z)
• Transform Once: Efficient Operator Learning in Frequency Domain [69.74509540521397]
  We study deep neural networks designed to harness the structure in frequency domain for efficient learning of long-range correlations in space or time. This work introduces a blueprint for frequency domain learning through a single transform: transform once (T1)
  arXiv  Detail & Related papers  (2022-11-26T01:56:05Z)
• Adaptive Frequency Learning in Two-branch Face Forgery Detection [66.91715092251258]
  We propose Adaptively learn Frequency information in the two-branch Detection framework, dubbed AFD. We liberate our network from the fixed frequency transforms, and achieve better performance with our data- and task-dependent transform layers.
  arXiv  Detail & Related papers  (2022-03-27T14:25:52Z)
• Deep Frequency Filtering for Domain Generalization [55.66498461438285]
  Deep Neural Networks (DNNs) have preferences for some frequency components in the learning process. We propose Deep Frequency Filtering (DFF) for learning domain-generalizable features. We show that applying our proposed DFF on a plain baseline outperforms the state-of-the-art methods on different domain generalization tasks.
  arXiv  Detail & Related papers  (2022-03-23T05:19:06Z)
• Rethinking FUN: Frequency-Domain Utilization Networks [21.10493050675827]
  We present FUN, a family of novel Frequency-domain Utilization Networks. These networks utilize the inherent efficiency of the frequency-domain by working directly in that domain. We show that working in frequency domain allows for dynamic compression of the input at inference time without any explicit change to the architecture.
  arXiv  Detail & Related papers  (2020-12-06T19:16:37Z)
• F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams [53.70940420595329]
  We propose F-FADE, a new approach for detection of anomalies in edge streams. It uses a novel frequency-factorization technique to efficiently model the time-evolving distributions of frequencies of interactions between node-pairs. F-FADE is able to handle in an online streaming setting a broad variety of anomalies with temporal and structural changes, while requiring only constant memory.
  arXiv  Detail & Related papers  (2020-11-09T19:55:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.