Toward Complex-Valued Neural Networks for Waveform Generation
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2026
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| author | Oh, Hyung-Seok Cho, Deok-Hyeon Kim, Seung-Bin Lee, Seong-Whan |
| author_facet | Oh, Hyung-Seok Cho, Deok-Hyeon Kim, Seung-Bin Lee, Seong-Whan |
| contents | Neural vocoders have recently advanced waveform generation, yielding natural and expressive audio. Among these approaches, iSTFT-based vocoders have recently gained attention. They predict a complex-valued spectrogram and then synthesize the waveform via iSTFT, thereby avoiding learned upsampling stages that can increase computational cost. However, current approaches use real-valued networks that process the real and imaginary parts independently. This separation limits their ability to capture the inherent structure of complex spectrograms. We present ComVo, a Complex-valued neural Vocoder whose generator and discriminator use native complex arithmetic. This enables an adversarial training framework that provides structured feedback in complex-valued representations. To guide phase transformations in a structured manner, we introduce phase quantization, which discretizes phase values and regularizes the training process. Finally, we propose a block-matrix computation scheme to improve training efficiency by reducing redundant operations. Experiments demonstrate that ComVo achieves higher synthesis quality than comparable real-valued baselines, and that its block-matrix scheme reduces training time by 25%. Audio samples and code are available at https://hs-oh-prml.github.io/ComVo/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11589 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Toward Complex-Valued Neural Networks for Waveform Generation Oh, Hyung-Seok Cho, Deok-Hyeon Kim, Seung-Bin Lee, Seong-Whan Sound Artificial Intelligence Neural vocoders have recently advanced waveform generation, yielding natural and expressive audio. Among these approaches, iSTFT-based vocoders have recently gained attention. They predict a complex-valued spectrogram and then synthesize the waveform via iSTFT, thereby avoiding learned upsampling stages that can increase computational cost. However, current approaches use real-valued networks that process the real and imaginary parts independently. This separation limits their ability to capture the inherent structure of complex spectrograms. We present ComVo, a Complex-valued neural Vocoder whose generator and discriminator use native complex arithmetic. This enables an adversarial training framework that provides structured feedback in complex-valued representations. To guide phase transformations in a structured manner, we introduce phase quantization, which discretizes phase values and regularizes the training process. Finally, we propose a block-matrix computation scheme to improve training efficiency by reducing redundant operations. Experiments demonstrate that ComVo achieves higher synthesis quality than comparable real-valued baselines, and that its block-matrix scheme reduces training time by 25%. Audio samples and code are available at https://hs-oh-prml.github.io/ComVo/. |
| title | Toward Complex-Valued Neural Networks for Waveform Generation |
| topic | Sound Artificial Intelligence |
| url | https://arxiv.org/abs/2603.11589 |