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Compressive sensing for perceptually correct reconstruction of music and speech signals

机译：对音乐和语音信号的感知矫正重建的压缩感测

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Compressive sensing (CS) is a technique that can achieve exact signal reconstruction by using fewer samples than those in the Nyquist theorem. In this study, listening tests were conducted to investigate the minimum number of samples needed for perceptually correct reconstruction by means of CS. The dictionary used as a sparsity domain for the signal reconstruction was the discrete cosine transform, and the reconstruction approach was the one provided by the L1-Magic. Three music signals and four speech signals were used as source signals. These signals were reconstructed by CS using different percentages of Nyquist samples. The results of the listening tests showed that, when 50% of the samples were used for the CS reconstruction, half of the test listeners judged the original and reconstructed signals to be perceptually the same. Listeners with musical training showed better sensitivity in distinguishing the original signals from the reconstructed signals than listeners without musical training. The log spectral distance between the original and reconstructed signals was a better objective index than the root mean square error and signal-to-noise ratio to evaluate the performance of the CS because inaccurate signal reconstruction mainly appeared at high frequencies. (C) 2021 Elsevier Ltd. All rights reserved.
机译：压缩检测（CS）是一种能够通过使用比奈奎斯特定理中的样品更少的样本来实现精确信号重建的技术。在这项研究中，进行了聆听测试，以研究通过CS对感知上正确重建所需的最小样品数。用作信号重建的稀疏域的字典是离散余弦变换，重建方法是L1-Magic提供的。使用三个音乐信号和四个语音信号作为源信号。 CS使用不同百分比的奈奎斯特样本来重建这些信号。听力测试的结果表明，当50％的样品用于CS重建时，一半的测试听众判断出原始的和重建信号是感知的。具有音乐训练的听众表现出更好的敏感性，在没有音乐训练的情况下将原始信号区分开于重构信号的原始信号。原始和重建信号之间的日志光谱距离是比根均方误差和信噪比更好的目标指标，以评估CS的性能，因为不准确的信号重建主要出现在高频下。 （c）2021 elestvier有限公司保留所有权利。

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