Quality Enhancement of Highly Degraded Music Using Deep Learning-Based Prediction Models for Lost Frequencies

A. Serra, A. Busson, Alan Livio Vasconcelos Guedes, S. Colcher
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引用次数: 2

Abstract

Audio quality degradation can have many causes. For musical applications, this fragmentation may lead to highly unpleasant experiences. Restoration algorithms may be employed to reconstruct missing parts of the audio in a similar way as for image reconstruction --- in an approach called audio inpainting. Current state-of-the art methods for audio inpainting cover limited scenarios, with well-defined gap windows and little variety of musical genres. In this work, we propose a Deep-Learning-based (DL-based) method for audio inpainting accompanied by a dataset with random fragmentation conditions that approximate real impairment situations. The dataset was collected using tracks from different music genres to provide a good signal variability. Our best model improved the quality of all musical genres, obtaining an average of 12.9 dB of PSNR, although it worked better for musical genres in which acoustic instruments are predominant.
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使用基于深度学习的丢失频率预测模型来提高高度退化音乐的质量
音频质量下降的原因有很多。对于音乐应用程序,这种分裂可能会导致非常不愉快的体验。恢复算法可以用来重建音频的缺失部分,以类似于图像重建的方式-在一种称为音频修复的方法中。目前最先进的音频绘制方法覆盖了有限的场景,具有明确的间隙窗口和很少的音乐类型。在这项工作中,我们提出了一种基于深度学习(dl)的音频修复方法,该方法伴随着一个具有近似真实损伤情况的随机碎片条件的数据集。数据集是使用来自不同音乐流派的曲目收集的,以提供良好的信号可变性。我们的最佳模型提高了所有音乐类型的质量,平均获得12.9 dB的PSNR,尽管它在原声乐器占主导地位的音乐类型中效果更好。
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