不同脉冲密度下天鹅绒噪声及其变体的感知研究

V. Välimäki, Heidi-Maria Lehtonen, M. Takanen
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引用次数: 33

摘要

本文研究了稀疏噪声序列,包括先前提出的天鹅绒噪声及其新变体。所有序列都只包含- 1、0和+ 1的采样值,并且每个脉冲的位置和符号是随机选择的。提出的两种算法是原始天鹅绒噪声的直接变体,需要两个随机数序列来确定脉冲的位置和符号。在其中一种算法中,脉冲的位置和符号是从同一随机数序列中提取的,这在实现上是有利的。此外,其中两个新序列包含已知的零区域。在听力测试中,测试对象将噪声序列与参考信号高斯白噪声进行比较,从而研究了所提出序列的感知平滑性。结果表明,在每秒2000个脉冲时,原始丝绒噪声比参考噪声听起来更平滑。在每秒4000个脉冲时,也有三种算法被认为比高斯噪声序列更平滑。这些观察结果可用于嘈杂声音的合成和人工混响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Perceptual Study on Velvet Noise and Its Variants at Different Pulse Densities
This paper investigates sparse noise sequences, including the previously proposed velvet noise and its novel variants defined here. All sequences consist of sample values minus one, zero, and plus one only, and the location and the sign of each impulse is randomly chosen. Two of the proposed algorithms are direct variants of the original velvet noise requiring two random number sequences for determining the impulse locations and signs. In one of the proposed algorithms the impulse locations and signs are drawn from the same random number sequence, which is advantageous in terms of implementation. Moreover, two of the new sequences include known regions of zeros. The perceived smoothness of the proposed sequences was studied with a listening test in which test subjects compared the noise sequences against a reference signal that was a Gaussian white noise. The results show that the original velvet noise sounds smoother than the reference at 2000 impulses per second. At 4000 impulses per second, also three of the proposed algorithms are perceived smoother than the Gaussian noise sequence. These observations can be exploited in the synthesis of noisy sounds and in artificial reverberation.
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来源期刊
IEEE Transactions on Audio Speech and Language Processing
IEEE Transactions on Audio Speech and Language Processing 工程技术-工程:电子与电气
自引率
0.00%
发文量
0
审稿时长
24.0 months
期刊介绍: The IEEE Transactions on Audio, Speech and Language Processing covers the sciences, technologies and applications relating to the analysis, coding, enhancement, recognition and synthesis of audio, music, speech and language. In particular, audio processing also covers auditory modeling, acoustic modeling and source separation. Speech processing also covers speech production and perception, adaptation, lexical modeling and speaker recognition. Language processing also covers spoken language understanding, translation, summarization, mining, general language modeling, as well as spoken dialog systems.
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