重叠-添加免费音乐噪声分析-合成系统

Y. Lam, R. Stewart
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引用次数: 1

摘要

最近,一种音频信号的分析合成系统使用基于正弦模型的算法进行谐波分量重建,并使用随机分量合成的噪声建模来生成高质量的合成信号。然而,目前的方法主要是使用相位随机化噪声驱动的源滤波器模型和重叠添加技术,这降低了由高度时间局域事件(如瞬态)组成的信号的性能。首先,解决了窗口重叠加分析合成对非平稳信号的不良影响;其次,提出了基于相干损耗补偿概念的无重叠加分析合成系统;第三,利用频域随机间隔的正弦分量和新的相位预测和相位校正技术,描述了基于感知机制的噪声合成模型。结果表明,对于高度时间局域化的信号,我们的无重叠加噪声合成模型比传统的相位随机化窗口叠加加噪声驱动滤波器模型具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An overlap-add free musical noise analysis-synthesis system
An analysis-synthesis system for audio signals using sinusoidal model-based algorithms for harmonic component reconstruction and noise modeling for stochastic component synthesis has recently demonstrated the generation of high-quality synthetic signals. However current approaches, which mainly use phase randomization noise-driven source filter models with overlap-add techniques, have degraded the performance for signals consisting of highly time-localized events such as transients. First, the undesirable effects of window overlap-add analysis-synthesis on non-stationary signals are addressed, second, an overlap-add-free analysis-synthesis system is proposed based on a coherent loss compensation concept, and third, a noise synthesis model based on the perceptual mechanism is described using sinusoidal components randomly spaced in the frequency domain and novel phase prediction and phase correction techniques. The listening results illustrate our overlap-add free noise synthesis model has better performance than the conventional window overlap-add noise-driven filter models with phase randomization for highly time-localized signals.
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