Speech-Declipping Transformer with Complex Spectrogram and Learnerble Temporal Features
<|reference_start|>Speech-Declipping Transformer with Complex Spectrogram and Learnerble Temporal Features: 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.<|reference_end|>
@article{kwon2024speech-declipping,
    title={Speech-Declipping Transformer with Complex Spectrogram and Learnerble
  Temporal Features},
    author={Younghoo Kwon, Jung-Woo Choi},
    journal={arXiv preprint arXiv:2409.12416},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12416},
    primaryClass={eess.AS cs.SD eess.SP}
}