利用audioreadto read in an impulse response recording. Create adsp.FrequencyDomainFirfilter对象使用已知的脉冲响应执行频域过滤。

[IRKONWONK，FS] = Audioread（'ChurchImperserSponse-16-44p1-Mono-5secs.wav'); systemModel = dsp.FrequencyDomainFIRFilter(irKnown');

通过使用mls功能。MLS激发信号必须比脉冲响应更长。请注意，MLS激发的长度扩展到了两个负元的下一个功率。

激发= mls（numel（irkone）+1）;情节（激发）标题（'Excitation')

Replicate the excitation signal four times to measure the average of three measurements. The recording of the first MLS sequence does include all the impulse response information, soimpzestdiscards it as a warmup run. Pad the excitation signal with zeros to account for the filter latency.

numRuns = 4; excrep = repmat(excitation,numRuns,1); excrep = [excrep;zeros(numel(irKnown)+1,1)];

将激发信号传递通过已知过滤器，然后添加噪声以建模真实的记录（系统响应）。切割过滤器在开头引入的延迟。

rec = systemModel(excrep); rec = rec + 0.1*randn(size(rec)); rec = rec(numel(irKnown)+2:end,:); plot(rec) title('System Response')

在现实世界中，MLS序列在录制时测试的系统中播放。记录将被切割，以便从拾取MLS序列开始并截断以持续重复序列的持续时间。

Pass the excitation signal and the system response to theimpzestfunction to estimate the impulse response. Plot the known impulse response and the simulation of the estimated impulse response for comparison.

irestimate = impzest（激发，rec）;样本= 1：numel（irkone）;情节（样品，生气（样品），'bo',...samples,irKnown(samples),“ m。”) legend('Known impulse response',“估计脉冲响应的模拟”)

生成一个长度为2^14-1样品的MLS信号，其水平为-5 dB。

L = 2^14-1;水平= -5;激发= mls（l，“激发”,level);

Visualize the excitation in time and time-frequency. For the time-domain plot, plot only the first 200 samples for visibility. The pattern is constant.

plot(excitation(1:200))

频谱图（激发，512,0,1024，'yaxis')