A Non-Time-Progressive Partial Tracking Algorithm for Sinusoidal Modeling
In this paper we propose a new sinusoidal model tracking algorithm that implements a non-time-progressive way of data processing. Sinusoidal partial parameters are estimated in the consecutive frames; however, the order of establishing individual connections between partials is determined by a greedy rule within the whole signal or within a specific time window. In this way, the strongest connections may be determined early, and subsequent predictions of each trajectory evolution are based on a more reliable partial evolution history, compared to a traditional progressive scheme. As a consequence, the proposed non-progressive tracking algorithm offers a statistically significant improvement of obtained trajectories in terms of better classic pattern recognition measures
Click to purchase paper or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!
This paper costs $33 for non-members, $5 for AES members and is free for E-Library subscribers.