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Toward Complex-Valued Neural Networks for Waveform Generation
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- Display score
- 9/10
- Last proof check
- 2026-03-17
- Score updated
- 2026-04-02
- Score fresh until
- 2026-05-02
- References
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- 0
- Coverage
- 33%
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Agent Handoff
Toward Complex-Valued Neural Networks for Waveform Generation
Canonical ID toward-complex-valued-neural-networks-for-waveform-generation | Route /signal-canvas/toward-complex-valued-neural-networks-for-waveform-generation
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/toward-complex-valued-neural-networks-for-waveform-generationMCP example
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Dimensions overall score 9.0
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Claim map
- Evidencepartial
We present ComVo, a Complex-valued neural Vocoder whose generator and discriminator use native complex arithmetic.
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- Evidencepartial
To guide phase transformations in a structured manner, we introduce phase quantization, which discretizes phase values and regularizes the training process.
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- Evidencepartial
Finally, we propose a block-matrix computation scheme to improve training efficiency by reducing redundant operations.
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- Evidencepartial
Experiments demonstrate that ComVo achieves higher synthesis quality than comparable real-valued baselines
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- Evidencepartial
its block-matrix scheme reduces training time by 25%
ImplicationmissingImplication not extracted yet.
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- Evidencepartial
This separation limits their ability to capture the inherent structure of complex spectrograms.
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- Evidencepartial
They predict a complex-valued spectrogram and then synthesize the waveform via iSTFT, thereby avoiding learned upsampling stages that can increase computational cost.
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- Evidencepartial
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.
ImplicationpartialDirectly stated in abstract as a limitation of existing approaches
Verificationpartialpartial
- Evidencepartial
Experiments demonstrate that ComVo achieves higher synthesis quality than comparable real-valued baselines
ImplicationpartialDirectly stated in abstract with experimental support mentioned
Verificationpartialpartial
- Evidencepartial
its block-matrix scheme reduces training time by 25%
ImplicationpartialDirect numeric claim with specific percentage improvement stated in abstract
Verificationpartialpartial
- Evidencepartial
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.
ImplicationpartialDirect description of method's core innovation in abstract
Verificationpartialpartial
- Evidencepartial
we introduce phase quantization, which discretizes phase values and regularizes the training process
ImplicationpartialDirect description of specific technical innovation in abstract
Verificationpartialpartial
