Results for Noise-Free Condition, Single Signal Component

In figure 3, we see the results generated by each estimator when noise is not present. In the plot:

• We use 31 positive $\alpha$ values evenly spaced on an logarithmic scale from $2\pi/F\cdot 2^{-3}$ to $2\pi/F\cdot 2^3$.
• $F$ is the sampling frequency (here 16000 Hz), $K = 1024$, and $N = 201$.
• We see how each estimator - including the neural net estimator - functions reliably only for its specific intended range of $\alpha$.
• The figure reveals that the Fresnel and Taylor model estimators behave consistently when given data with $\alpha$ outside their intended regions; this will make the decision of which estimator to use for a given signal near-trivial.

Figure 3: Estimators without noise.