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.
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|320||International Conference||Jihyun Lee, Hyungseob Lim, Chanwoo Lee, Inseon Jang, Hong-Goo Kang "Adversarial Audio Synthesis Using a Harmonic-Percussive Discriminator" in ICASSP, 2022|
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|318||International Conference||Huu-Kim Nguyen, Kihyuk Jeong, Se-Yun Um, Min-Jae Hwang, Eunwoo Song, Hong-Goo Kang "LiteTTS: A Decoder-free Light-weight Text-to-wave Synthesis Based on Generative Adversarial Networks" in INTERSPEECH, 2021|
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|316||International Conference||Miseul Kim, Minh-Tri Ho, Hong-Goo Kang "Self-supervised Complex Network for Machine Sound Anomaly Detection" in EUSIPCO, 2021|
|315||International Conference||Kihyuk Jeong, Huu-Kim Nguyen, Hong-Goo Kang "A Fast and Lightweight Text-To-Speech Model with Spectrum and Waveform Alignment Algorithms" in EUSIPCO, 2021|
|314||International Conference||Jiyoung Lee*, Soo-Whan Chung*, Sunok Kim, Hong-Goo Kang**, Kwanghoon Sohn** "Looking into Your Speech: Learning Cross-modal Affinity for Audio-visual Speech Separation" in CVPR, 2021|
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