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The Restoration of Low-Quality Audio Recordings Based on Non-Negative Matrix Factorization and Perceptual Assessment by Means of the EBU MUSHRA Test Method

机译:基于非负矩阵分解和基于EBU MUSHRA测试方法的感知评估的低质量音频记录的恢复

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In this paper, we focus on the signal-to-noise ratio (SNR) improvement in single channel audio recordings. Many approaches have been reported in the literature. The most popular method, with many variants, is Short Time Spectral Attenuation (STSA). Although this method reduces the noise and improves the SNR, it mostly tends to introduce signal distortion and a perceptually annoying residual noise usually called musical noise. In this paper we investigate the use of Non-negative Matrix Factorization (NMF) as an alternative to the STSA for the digital curation of musical heritage. NMF is an emerging new technique in the blind extraction of signals recorded in a variety of different fields. The application of NMF to the analysis of monaural recordings is relatively recent. We show that NMF is a suitable technique to extract the clean audio signal from undesired non stationary noise in a monaural recording of ethnic music. More specifically, we introduce a perceptual suppression rule to determine how the perceptual domain is competitive compared to the acoustic domain. Moreover, we carry out a listening test in order to compare NMF with the state of the art audio restoration framework using the EBU MUSHRA test method. The encouraging results obtained with this methodology in the presented case study support their wider applicability in audio separation.
机译:在本文中,我们专注于单通道音频记录中的信噪比(SNR)的改善。文献中已经报道了许多方法。带有多种变体的最受欢迎的方法是短时光谱衰减(STSA)。尽管此方法减少了噪声并提高了SNR,但它通常会引入信号失真和通常被称为音乐噪声的令人讨厌的残留噪声。在本文中,我们研究了使用非负矩阵分解(NMF)作为STSA的替代品来进行音乐遗产的数字管理。 NMF是一种盲目提取在各种不同领域中记录的信号的新兴技术。 NMF在单声道录音分析中的应用是相对较新的。我们表明NMF是一种从民族音乐的单声道录音中从不希望的非平稳噪声中提取干净音频信号的合适技术。更具体地说,我们引入了感知抑制规则来确定感知域与声学域相比如何具有竞争力。此外,我们进行了听力测试,以便使用EBU MUSHRA测试方法将NMF与最新的音频恢复框架进行比较。在本案例研究中使用这种方法所获得的令人鼓舞的结果支持了它们在音频分离中的广泛应用。

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