The paper is organized as follows: "Related work" section shows related work, and "Issues and approach" section explains issues and our approach.
For pitch estimation, eleven approaches are compared: four classification approaches with auditory features RF or SVM combined with DFT or ACF features—(Section 4.3.1), two peak-picking variants for the SACF approach (Section 4.3.2) and five variants of the YIN (respectively pYIN) algorithm as the state-of-the-art approaches without an auditory model.
Thereafter we present research approach (Section 3), results and analysis (Section 4), discussion (Section 5), followed by conclusions and future work (Section 6).
Using a progressive approach, Section 5 explains and formally verifies the refinement stages, which led to the final version of the protocol.
The paper is organized as follows: Section 2 presents the data we use and describes our methodological approach, Section 3 presents our findings, and Section 4 concludes.
In this section, we evaluate the numerical results obtained by the approach discussed in the 'Suggested cutting approach' section of this article and by some pre-existing methods in the literature on rectangular sheets and parts (Whitwell 2004).
The proposed algorithm can also be utilized to adaptively form the sub-beams of the four quadrant monopulse approach (Section 2 in [3]) and the multiple squinted beams approach (Section 3 in [51]) to improve the robustness of these sub-beams to pattern look direction error when the interference-plus-noise covariance matrix is unavailable.
"The proposed approach" section explains how DAC works, and "Experimental evaluation" section describes the experimental evaluation of DAC.
To better express the proposed approach, Section 2 introduces the background and provides a formalized definition of the problem.
The our approach section discusses about community dynamics, research approach and complex network structural parameters.
