Improving Speech Enhancement by Integrating Inter-Channel and Band Features with Dual-branch Conformer
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909253337350144 |
|---|---|
| author | Li, Jizhen Xu, Xinmeng Tu, Weiping Yang, Yuhong Zhu, Rong |
| author_facet | Li, Jizhen Xu, Xinmeng Tu, Weiping Yang, Yuhong Zhu, Rong |
| contents | Recent speech enhancement methods based on convolutional neural networks (CNNs) and transformer have been demonstrated to efficaciously capture time-frequency (T-F) information on spectrogram. However, the correlation of each channels of speech features is failed to explore. Theoretically, each channel map of speech features obtained by different convolution kernels contains information with different scales demonstrating strong correlations. To fill this gap, we propose a novel dual-branch architecture named channel-aware dual-branch conformer (CADB-Conformer), which effectively explores the long range time and frequency correlations among different channels, respectively, to extract channel relation aware time-frequency information. Ablation studies conducted on DNS-Challenge 2020 dataset demonstrate the importance of channel feature leveraging while showing the significance of channel relation aware T-F information for speech enhancement. Extensive experiments also show that the proposed model achieves superior performance than recent methods with an attractive computational costs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06524 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Speech Enhancement by Integrating Inter-Channel and Band Features with Dual-branch Conformer Li, Jizhen Xu, Xinmeng Tu, Weiping Yang, Yuhong Zhu, Rong Sound Multimedia Audio and Speech Processing Recent speech enhancement methods based on convolutional neural networks (CNNs) and transformer have been demonstrated to efficaciously capture time-frequency (T-F) information on spectrogram. However, the correlation of each channels of speech features is failed to explore. Theoretically, each channel map of speech features obtained by different convolution kernels contains information with different scales demonstrating strong correlations. To fill this gap, we propose a novel dual-branch architecture named channel-aware dual-branch conformer (CADB-Conformer), which effectively explores the long range time and frequency correlations among different channels, respectively, to extract channel relation aware time-frequency information. Ablation studies conducted on DNS-Challenge 2020 dataset demonstrate the importance of channel feature leveraging while showing the significance of channel relation aware T-F information for speech enhancement. Extensive experiments also show that the proposed model achieves superior performance than recent methods with an attractive computational costs. |
| title | Improving Speech Enhancement by Integrating Inter-Channel and Band Features with Dual-branch Conformer |
| topic | Sound Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2407.06524 |