Problems of reconstructing acoustic signals from unevenly spaced samples (sparse signal) and signals distorted by losses of adjacent samples (solid gaps) are considered. To solve these problems original algorithms are proposed: the Interpolation Method of Sequential Computation of the Fourier spectrum and the method of amplitude iterations. The effectiveness of these methods is compared with the method of projections onto convex sets and its modification, implemented using an evolutionary time-frequency transformation based on the basic functions of Slepian. In the case of continuous gaps, comparisons are made with a one-dimensional modification of the method, which is related to neural networks image inpainting. Evaluation of the effectiveness of the proposed methods according to objective criteria indicates their suitability for practical use.

考虑了从不均匀间隔的样本（稀疏信号）和由于相邻样本的丢失而失真的信号（实心间隙）重构声信号的问题。为了解决这些问题，提出了原始算法：傅立叶频谱的顺序计算的插值方法和幅度迭代的方法。将这些方法的有效性与投影到凸集上的方法及其修改方法进行了比较，这些方法是使用基于Slepian基本函数的演化时频变换实现的。在连续间隙的情况下，使用该方法的一维修改进行比较，这与神经网络图像修复有关。根据客观标准对所提出方法的有效性进行评估，表明它们对实际应用的适用性。