Cumulative Spectral Analysis for Transient Decaying Signals in a Transmission System Including a Feedback Loop
Cumulative Spectral Analysis for Transient Decaying Signals in a Transmission System Including a Feedback Loop
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包括反馈环路的传输系统中瞬态衰减信号的累积频谱分析
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发表时间：
2006
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通讯作者：
Y. Yamasaki
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作者：
Yoshinori Takahashi;M. Tohyama;Y. Yamasaki
瞬态衰减信号部分的累积谱分析（CSA）是一种有效的方法来检测频谱失真，并在公共广播系统开始啸叫之前快速确定其主谐振频率。由于反馈回路中的周期性延迟而引起的光谱失真，即所谓的着色，可以通过观察信号的光谱累积过程来检测，所述光谱失真可能引起回路的啸叫。CSA最初由Berman和芬查姆提出，用于扬声器的瞬态分析。通过在CSA中引入谱累积函数，即累积谐波分析（CHA），对谱累积过程进行了研究，使谱累积过程更加直观。CSA所揭示的信号或脉冲响应的谱积累效应比CHA所发现的要小一些。因此，虽然CHA所拾取的用于衰减语音信号部分的主频分量的频谱频率分布清楚地显示了由于反馈语音信号而引起的着色，但是它仍然只能通过收听而被轻微地感知。因此，通过CHA或通过常规CSA对信号样本的短衰减段的频率分布分析可以用于在现场条件下的啸声频率的盲预测，而无需传递函数和原始输入信号的详细说明。对于未来的工作而言，有必要研究需要多长的观测间隔，以及什么样的累积函数对预测啸叫频率是有效的。特别是，多个输入和输出系统，包括混响条件下的时变闭环的模拟实验，将是必要的评估所提出的方法从实用的角度来看。
Cumulative spectral analysis (CSA) of transient decaying signal portions is an effective approach to detecting spectral distortion and to determine quickly the principal resonant frequency of a public-address system before it starts howling. Spectral distortion, so-called coloration, due to periodic delays in a feedback loop, which might cause howling of the loop, could be detected by observing a spectral-accumulation process of the signals. CSA was originally proposed by Berman and Fincham for transient analysis of loudspeakers. The cumulative spectral process is investigated by introducing a spectral accumulation function into CSA, called cumulative harmonic analysis (CHA), so that the spectral accumulation process might be visualized effectively. The spectral accumulation effect of signals or impulse responses revealed by CSA is a little less than that found when using CHA. Consequently while a spectral-frequency distribution of the dominant frequency components picked up by CHA for decaying speech-signal portions clearly displays the coloration due to feedback speech signals, it can nevertheless be only slightly perceived by listening. Thus frequency distribution analysis by CHA or by conventional CSA for short decaying segments of signal samples can be useful in the blind prediction of the howling frequency without detailed specifications of the transfer functions and the original input signals under in situ conditions. As future work is concerned, it is necessary to investigate how long an observation interval would be required, and what kind of accumulation function is effective to predict howling frequencies. In particular, simulation experiments for multiple input and output systems, including time-variant closed loops under reverberation conditions, would be necessary for evaluating the proposed method from a practical point of view.