Name
Title | ||
|---|---|---|
Resonance-Based Time-Frequency Manifold for Feature Extraction of Ship-Radiated Noise. |
Abstract | ||
|---|---|---|
In this paper, a novel time-frequency signature using resonance-based sparse signal decomposition (RSSD), phase space reconstruction (PSR),time-frequency distribution (TFD) and manifold learning is proposed for feature extraction of ship-radiated noise, which is called resonance-based time-frequency manifold (RTFM). This is suitable for analyzing signals with oscillatory, non-stationary and non-linear characteristics in a situation of serious noise pollution. Unlike the traditional methods which are sensitive to noise and just consider one side of oscillatory, non-stationary and non-linear characteristics, the proposed RTFM can provide the intact feature signature of all these characteristics in the form of a time-frequency signature by the following steps: first, RSSD is employed on the raw signal to extract the high-oscillatory component and abandon the low-oscillatory component. Second, PSR is performed on the high-oscillatory component to map the one-dimensional signal to the high-dimensional phase space. Third, TFD is employed to reveal non-stationary information in the phase space. Finally, manifold learning is applied to the TFDs to fetch the intrinsic non-linear manifold. A proportional addition of the top two RTFMs is adopted to produce the improved RTFM signature. All of the case studies are validated on real audio recordings of ship-radiated noise. Case studies of ship-radiated noise on different datasets and various degrees of noise pollution manifest the effectiveness and robustness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18040936 | SENSORS |
Keywords | Field | DocType |
ship-radiated noise,resonance-based sparse signal decomposition,manifold learning,phase space reconstruction,resonance-based time-frequency manifold | Phase space,Algorithm,Robustness (computer science),Feature extraction,Electronic engineering,Fetch,Time–frequency analysis,Engineering,Nonlinear dimensionality reduction,Noise pollution,Manifold | Journal |
Volume | Issue | Citations |
18 | 4.0 | 1 |
PageRank | References | Authors |
0.44 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiaquan Yan | 1 | 1 | 0.44 |
| sun haixin | 2 | 14 | 11.33 |
| Hailan Chen | 3 | 1 | 0.44 |
| Naveed Ur Rehman Junejo | 4 | 5 | 1.52 |
| En Cheng | 5 | 152 | 20.81 |