Adversarial Audio Synthesis Using a Harmonic-Percussive Discriminator
In this paper, we propose a discriminator design scheme for generative adversarial network (GAN)-based audio signal generation.
Unlike conventional discriminators which take an entire signal as input, our discriminator design separates the audio signal into harmonic and percussive components and analyzes each component independently.
The rationale behind this idea is that conventional discriminators cannot reliably capture subtle distortions in general audio signals, which have complicated time-frequency characteristics.
By considering the time-frequency resolution of audio signals, our proposed method encourages the generator to better reconstruct harmonic and percussive features, which are critical for the quality of the generated signals.
Listening tests show that our framework significantly enhances the stability of pitches and generates clearer audio compared to a baseline.
|334||International Conference||Doyeon Kim, Soo-Whan Chung, Hyewon Han, Youna Ji, Hong-Goo Kang "HD-DEMUCS: General Speech Restoration with Heterogeneous Decoders" in INTERSPEECH, 2023|
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|332||International Conference||Byeong Hyeon Kim, Hyungseob Lim, Jihyun Lee, Inseon Jang, Hong-Goo Kang "Progressive Multi-Stage Neural Audio Codec with Psychoacoustic Loss and Discriminator" in ICASSP, 2023|
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|329||International Journal||Jinyoung Lee, Hong-Goo Kang "Real-Time Neural Speech Enhancement Based on Temporal Refinement Network and Channel-Wise Gating Methods" in Digital Signal Processing, vol.133, 2023|
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|326||International Conference||Hyeon-Kyeong Shin, Hyewon Han, Doyeon Kim, Soo-Whan Chung, Hong-Goo Kang "Learning Audio-Text Agreement for Open-vocabulary Keyword Spotting" in INTERSPEECH (*Best Student Paper Finalist), 2022|
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