Speech-Declipping Transformer with Complex Spectrogram and Learnerble Temporal Features
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Kwon, Younghoo Choi, Jung-Woo |
| author_facet | Kwon, Younghoo Choi, Jung-Woo |
| contents | We present a transformer-based speech-declipping model that effectively recovers clipped signals across a wide range of input signal-to-distortion ratios (SDRs). While recent time-domain deep neural network (DNN)-based declippers have outperformed traditional handcrafted and spectrogram-based DNN approaches, they still struggle with low-SDR inputs. To address this, we incorporate a transformer-based architecture that operates in the time-frequency (TF) domain. The TF-transformer architecture has demonstrated remarkable performance in the speech enhancement task for low-SDR signals but cannot be optimal for the time-domain artifact like clipping. To overcome the limitations of spectrogram-based DNNs, we design an extra convolutional block that directly extracts temporal features from time-domain waveforms. The joint analysis of complex spectrogram and learned temporal features allows the model to improve performance on both high- and low-SDR inputs. Our approach also preserves the unclipped portions of the speech signal during processing, preventing degradation typically seen when only spectral information is used. In evaluations on the VoiceBank-DEMAND and DNS challenge datasets, the proposed model consistently outperformed state-of-the-art (SOTA) declipping models across various metrics, demonstrating its robustness and generalizability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12416 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Speech-Declipping Transformer with Complex Spectrogram and Learnerble Temporal Features Kwon, Younghoo Choi, Jung-Woo Audio and Speech Processing Sound Signal Processing We present a transformer-based speech-declipping model that effectively recovers clipped signals across a wide range of input signal-to-distortion ratios (SDRs). While recent time-domain deep neural network (DNN)-based declippers have outperformed traditional handcrafted and spectrogram-based DNN approaches, they still struggle with low-SDR inputs. To address this, we incorporate a transformer-based architecture that operates in the time-frequency (TF) domain. The TF-transformer architecture has demonstrated remarkable performance in the speech enhancement task for low-SDR signals but cannot be optimal for the time-domain artifact like clipping. To overcome the limitations of spectrogram-based DNNs, we design an extra convolutional block that directly extracts temporal features from time-domain waveforms. The joint analysis of complex spectrogram and learned temporal features allows the model to improve performance on both high- and low-SDR inputs. Our approach also preserves the unclipped portions of the speech signal during processing, preventing degradation typically seen when only spectral information is used. In evaluations on the VoiceBank-DEMAND and DNS challenge datasets, the proposed model consistently outperformed state-of-the-art (SOTA) declipping models across various metrics, demonstrating its robustness and generalizability. |
| title | Speech-Declipping Transformer with Complex Spectrogram and Learnerble Temporal Features |
| topic | Audio and Speech Processing Sound Signal Processing |
| url | https://arxiv.org/abs/2409.12416 |