# BSS-CFFMA: Cross-Domain Feature Fusion and Multi-Attention Speech Enhancement Network based on Self-Supervised Embedding

arXiv: https://arxiv.org/abs/2408.06851 · HTML: https://mlsanity.com/paper/2408.06851/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 41 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 26 of 26)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [Deep clustering: Discriminative embeddings for segmentation and separation](https://mlsanity.com/paper/1508.04306/) (2015)
- [SEGAN: Speech Enhancement Generative Adversarial Network](https://mlsanity.com/paper/1703.09452/) (2017)
- [Conditional Diffusion Probabilistic Model for Speech Enhancement](https://mlsanity.com/paper/2202.05256/) (2022)
- [MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement](https://mlsanity.com/paper/2104.03538/) (2021)
- [MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement](https://mlsanity.com/paper/1905.04874/) (2019)
- [Audio Self-supervised Learning: A Survey](https://mlsanity.com/paper/2203.01205/) (2022)
- [WHAMR!: Noisy and Reverberant Single-Channel Speech Separation](https://mlsanity.com/paper/1910.10279/) (2019)
- [Squeezeformer: An Efficient Transformer for Automatic Speech Recognition](https://mlsanity.com/paper/2206.00888/) (2022)
- [TSTNN: Two-stage Transformer based Neural Network for Speech Enhancement in the Time Domain](https://mlsanity.com/paper/2103.09963/) (2021)
- [Investigating self-supervised learning for speech enhancement and separation](https://mlsanity.com/paper/2203.07960/) (2022)
- [Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features](https://mlsanity.com/paper/2111.02363/) (2021)
- [MANNER: Multi-view Attention Network for Noise Erasure](https://mlsanity.com/paper/2203.02181/) (2022)
- [Boosting Self-Supervised Embeddings for Speech Enhancement](https://mlsanity.com/paper/2204.03339/) (2022)
- [Uformer: A Unet based dilated complex & real dual-path conformer network for simultaneous speech enhancement and dereverberation](https://mlsanity.com/paper/2111.06015/) (2021)
- [WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech Enhancement](https://mlsanity.com/paper/2004.04098/) (2020)
- [Self-Supervised Learning for Speech Enhancement through Synthesis](https://mlsanity.com/paper/2211.02542/) (2022)
- [Phase-aware Single-stage Speech Denoising and Dereverberation with U-Net](https://mlsanity.com/paper/2006.00687/) (2020)
- [Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing](https://mlsanity.com/paper/2401.02764/) (2024)
- [MFHCA: Enhancing Speech Emotion Recognition Via Multi-Spatial Fusion and Hierarchical Cooperative Attention](https://mlsanity.com/paper/2404.13509/) (2024)
- [Rethinking Gradient Operator for Exposing AI-enabled Face Forgeries](https://mlsanity.com/paper/2205.00767/) (2022)
- [Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Band Speech Enhancement](https://mlsanity.com/paper/2203.16033/) (2022)

## Built on by (most cited first, 10 of 10)

- [Magnitude-Phase Dual-Path Speech Enhancement Network based on Self-Supervised Embedding and Perceptual Contrast Stretch Boosting](https://mlsanity.com/paper/2503.21571/) (2025)
- [EAD-Net: Emotion-Aware Talking Head Generation with Spatial Refinement and Temporal Coherence](https://mlsanity.com/paper/2604.23325/) (2026)
- [ML-SAN: Multi-Level Speaker-Adaptive Network for Emotion Recognition in Conversations](https://mlsanity.com/paper/2604.25383/) (2026)
- [MRUF: Multi-granularity Routing with Uncertainty-Aware Fusion for Robust Multimodal Sentiment Analysis](https://mlsanity.com/paper/2607.10599/) (2026)
- [HRO: Hierarchical Room-to-Object Framework for Zero-Shot Object Goal Navigation with Large Language Models](https://mlsanity.com/paper/2607.13072/) (2026)
- [CTAN: Cycle-Temporal Attention Network for Embodied Audio-Visual Navigation](https://mlsanity.com/paper/2609.17420/) (2026)
- [Transformer-Based Token Fusion and Dynamic Graph Planning for Audio-Visual Navigation](https://mlsanity.com/paper/2609.17421/) (2026)
- [Talking Head Synthesis with Facial Landmark Guidance via 3D Gaussian Splatting](https://mlsanity.com/paper/2609.17422/) (2026)
- [WS-NeRF: A Mamba-Driven World-State-Aware Adaptive Deblurring Neural Radiance Field](https://mlsanity.com/paper/2609.21391/) (2026)
- [Two-Stage Multi-modal Learning for Dynamic Audio-Visual Navigation: From Physical Alignment to Scene Aggregation](https://mlsanity.com/paper/2610.14077/) (2026)

## Abstract (arXiv)

Speech self-supervised learning (SSL) represents has achieved state-of-the-art (SOTA) performance in multiple downstream tasks. However, its application in speech enhancement (SE) tasks remains immature, offering opportunities for improvement. In this study, we introduce a novel cross-domain feature fusion and multi-attention speech enhancement network, termed BSS-CFFMA, which leverages self-supervised embeddings. BSS-CFFMA comprises a multi-scale cross-domain feature fusion (MSCFF) block and a residual hybrid multi-attention (RHMA) block. The MSCFF block effectively integrates cross-domain features, facilitating the extraction of rich acoustic information. The RHMA block, serving as the primary enhancement module, utilizes three distinct attention modules to capture diverse attention representations and estimate high-quality speech signals. We evaluate the performance of the BSS-CFFMA model through comparative and ablation studies on the VoiceBank-DEMAND dataset, achieving SOTA results. Furthermore, we select three types of data from the WHAMR! dataset, a collection specifically designed for speech enhancement tasks, to assess the capabilities of BSS-CFFMA in tasks such as denoising only, dereverberation only, and simultaneous denoising and dereverberation. This study marks the first attempt to explore the effectiveness of self-supervised embedding-based speech enhancement methods in complex tasks encompassing dereverberation and simultaneous denoising and dereverberation. The demo implementation of BSS-CFFMA is available onlinefootnote[2]https://github.com/AlimMat/BSS-CFFMA. labels1.