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Let P(0) ( P(1) ) is a probability of transmitting bit zero (one); P(e|0) ( P(e|1) ) is a probability of error when detecting bit zero (one).
The probability of erroneous detection is
$$
P(e) = P(e|0)P(0) + P(e|1)P(1) \tag {1}
$$
Let $V_0$ ( $V_1$ ) be a nominal signal voltage of bit zero ( one ) signal at the transmitter.
$$
P(e|0) = \int_T^{\infty}{{\frac ...
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The answer to you question depends on how quality is defined in your specific application. Since you're working with audio signals, it is often beneficial to use the perceived quality to analyze the efficiency of an audio enhancement algorithm.
How to Determine the Perceived Quality
Methods to determine the perceived quality of an audio signal can be ...
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I don't think it's a good idea to add random noise to frequency response. In general, noise is added to the input or output signal. You should be clear that at which stage the noise is introduced. According to the different stages of noise introduction, there are two main transfer function estimators, $H_1$ estimator and $H_2$ estimator.
$H_1$ assumes that ...
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Well, this is one way you could do it: with LTspice, use the white() function. There are also rand() ([0,1] V, pulses) and random() ([0,1] V, smooth pulses), but white(2*fs*time) will give you what you want between [-0.5, 0.5] V:
B1 is a behavioural current source (bi, or bi2, as it appears in the component selection dialog, F2), and the .wavecommand ...
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Using real data is a way to validate the assumption that your algorithm will behave with real data in a similar way to the behavior with generated data.
In a general sense.
When we want to test an algorithm we can create tests, that are other algorithms that provide inputs and evaluate outputs of the algorithm we are design (your denoising algorithm).
...
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