SPMamba: State-space model is all you need in speech separation

• URL: http://arxiv.org/abs/2404.02063v2
• Date: Tue, 10 Sep 2024 14:02:58 GMT
• Title: SPMamba: State-space model is all you need in speech separation
• Authors: Kai Li, Guo Chen, Runxuan Yang, Xiaolin Hu,
• Abstract summary: CNN-based speech separation models face local receptive field limitations and cannot effectively capture long time dependencies. We introduce an innovative speech separation method called SPMamba. This model builds upon the robust TF-GridNet architecture, replacing its traditional BLSTM modules with bidirectional Mamba modules.
• Score: 20.168153319805665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing CNN-based speech separation models face local receptive field limitations and cannot effectively capture long time dependencies. Although LSTM and Transformer-based speech separation models can avoid this problem, their high complexity makes them face the challenge of computational resources and inference efficiency when dealing with long audio. To address this challenge, we introduce an innovative speech separation method called SPMamba. This model builds upon the robust TF-GridNet architecture, replacing its traditional BLSTM modules with bidirectional Mamba modules. These modules effectively model the spatiotemporal relationships between the time and frequency dimensions, allowing SPMamba to capture long-range dependencies with linear computational complexity. Specifically, the bidirectional processing within the Mamba modules enables the model to utilize both past and future contextual information, thereby enhancing separation performance. Extensive experiments conducted on public datasets, including WSJ0-2Mix, WHAM!, and Libri2Mix, as well as the newly constructed Echo2Mix dataset, demonstrated that SPMamba significantly outperformed existing state-of-the-art models, achieving superior results while also reducing computational complexity. These findings highlighted the effectiveness of SPMamba in tackling the intricate challenges of speech separation in complex environments.

Related papers

• DiffImp: Efficient Diffusion Model for Probabilistic Time Series Imputation with Bidirectional Mamba Backbone [6.428451261614519]
  Current DDPM-based probabilistic time series imputation methodologies are confronted with two types of challenges. We integrate the computational efficient state space model, namely Mamba, as the backbone denosing module for DDPMs. Our approach can achieve state-of-the-art time series imputation results on multiple datasets, different missing scenarios and missing ratios.
  arXiv  Detail & Related papers  (2024-10-17T08:48:52Z)
• PPMamba: A Pyramid Pooling Local Auxiliary SSM-Based Model for Remote Sensing Image Semantic Segmentation [1.5136939451642137]
  This paper proposes a novel network called Pyramid Pooling Mamba (PPMamba), which integrates CNN and Mamba for semantic segmentation tasks. PPMamba achieves competitive performance compared to state-of-the-art models.
  arXiv  Detail & Related papers  (2024-09-10T08:08:50Z)
• Bidirectional Gated Mamba for Sequential Recommendation [56.85338055215429]
  Mamba, a recent advancement, has exhibited exceptional performance in time series prediction. We introduce a new framework named Selective Gated Mamba ( SIGMA) for Sequential Recommendation. Our results indicate that SIGMA outperforms current models on five real-world datasets.
  arXiv  Detail & Related papers  (2024-08-21T09:12:59Z)
• LaMamba-Diff: Linear-Time High-Fidelity Diffusion Models Based on Local Attention and Mamba [54.85262314960038]
  Local Attentional Mamba blocks capture both global contexts and local details with linear complexity. Our model exhibits exceptional scalability and surpasses the performance of DiT across various model scales on ImageNet at 256x256 resolution. Compared to state-of-the-art diffusion models on ImageNet 256x256 and 512x512, our largest model presents notable advantages, such as a reduction of up to 62% GFLOPs.
  arXiv  Detail & Related papers  (2024-08-05T16:39:39Z)
• FMamba: Mamba based on Fast-attention for Multivariate Time-series Forecasting [6.152779144421304]
  We introduce a novel framework named FMamba for multivariate time-series forecasting (MTSF) Technically, we first extract the temporal features of the input variables through an embedding layer, then compute the dependencies among input variables via the fast-attention module. We use Mamba to selectively deal with the input features and further extract the temporal dependencies of the variables through the multi-layer perceptron block (MLP-block) Finally, FMamba obtains the predictive results through the projector, a linear layer.
  arXiv  Detail & Related papers  (2024-07-20T09:14:05Z)
• MambaForGCN: Enhancing Long-Range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-Based Sentiment Analysis [0.6885635732944716]
  We present a novel approach to enhance long-range dependencies between aspect and opinion words in ABSA (MambaForGCN) Experimental results on three benchmark datasets demonstrate MambaForGCN's effectiveness, outperforming state-of-the-art (SOTA) baseline models.
  arXiv  Detail & Related papers  (2024-07-14T22:23:07Z)
• DeciMamba: Exploring the Length Extrapolation Potential of Mamba [89.07242846058023]
  We introduce DeciMamba, a context-extension method specifically designed for Mamba. We show that DeciMamba can extrapolate context lengths 25x longer than the ones seen during training, and does so without utilizing additional computational resources.
  arXiv  Detail & Related papers  (2024-06-20T17:40:18Z)
• Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RL [57.202733701029594]
  Decision Mamba is a novel multi-grained state space model with a self-evolving policy learning strategy. To mitigate the overfitting issue on noisy trajectories, a self-evolving policy is proposed by using progressive regularization. The policy evolves by using its own past knowledge to refine the suboptimal actions, thus enhancing its robustness on noisy demonstrations.
  arXiv  Detail & Related papers  (2024-06-08T10:12:00Z)
• MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection [5.37935922811333]
  MambaMixer is a new architecture with data-dependent weights that uses a dual selection mechanism across tokens and channels. As a proof of concept, we design Vision MambaMixer (ViM2) and Time Series MambaMixer (TSM2) architectures based on the MambaMixer block.
  arXiv  Detail & Related papers  (2024-03-29T00:05:13Z)
• Simultaneous Machine Translation with Large Language Models [51.470478122113356]
  We investigate the possibility of applying Large Language Models to SimulMT tasks. We conducted experiments using the textttLlama2-7b-chat model on nine different languages from the MUST-C dataset. The results show that LLM outperforms dedicated MT models in terms of BLEU and LAAL metrics.
  arXiv  Detail & Related papers  (2023-09-13T04:06:47Z)
• Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis [96.46952672172021]
  Bi-Bimodal Fusion Network (BBFN) is a novel end-to-end network that performs fusion on pairwise modality representations. Model takes two bimodal pairs as input due to known information imbalance among modalities.
  arXiv  Detail & Related papers  (2021-07-28T23:33:42Z)

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.