تقرير
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement
| العنوان: | MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement |
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| المؤلفون: | Fu, Szu-Wei, Liao, Chien-Feng, Tsao, Yu, Lin, Shou-De |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing |
| الوصف: | Adversarial loss in a conditional generative adversarial network (GAN) is not designed to directly optimize evaluation metrics of a target task, and thus, may not always guide the generator in a GAN to generate data with improved metric scores. To overcome this issue, we propose a novel MetricGAN approach with an aim to optimize the generator with respect to one or multiple evaluation metrics. Moreover, based on MetricGAN, the metric scores of the generated data can also be arbitrarily specified by users. We tested the proposed MetricGAN on a speech enhancement task, which is particularly suitable to verify the proposed approach because there are multiple metrics measuring different aspects of speech signals. Moreover, these metrics are generally complex and could not be fully optimized by Lp or conventional adversarial losses. Comment: Accepted by Thirty-sixth International Conference on Machine Learning (ICML) 2019 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1905.04874 |
| رقم الأكسشن: | edsarx.1905.04874 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |