A study of singular spectrum analysis with global optimization techniques

Chu, Moody T.; Lin, Matthew M.; Wang, Liqi

doi:10.1007/s10898-013-0117-3

Cited by 9 publications

(29 citation statements)

References 54 publications

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“…When compared with other time series analysis methods, it shows certain superiority, potentials and broader application areas and competes with more standard methods in the area of time series analysis [27,30,39,40].…”

Section: Overviewmentioning

confidence: 99%

“…The SSA is a model free and non-parametric method consists of two complementary stages which are decomposition and reconstruction [27,29,40,41], as shown in Figure 1. The method is mainly based on statistical approaches and many basic operations of the SSA algorithm are elementary linear algebra [12,31,40,42]. The key element in the de-nosing process is to remove the noise without losing a significant portion of the signal and this can be accomplished with the SSA [12,33].…”

Section: Overviewmentioning

confidence: 99%

“…The main aspect in studying the properties of the SSA is the separability, which describes how well different components can be separated from each other [23,27,43]. If the dataset is separable, the focus of attention must be how to select proper grouping criteria after identifying suitable window length [12,40].…”

Section: Overviewmentioning

confidence: 99%

“…A harmonic component commonly produces two eigentriples with close singular values; however, a pure noise series typically produces a gradually decreasing sequence of singular values. Furthermore, checking breaks in the eigenvalue spectra is also another useful insight [12,27,40,44]. This can be accomplished by using visual SSA tool in the eigenvalue spectra.…”

Section: The Ssa Theorymentioning

confidence: 99%

“…In other words, checking breaks is to ensure the availability of eigentriples with nearly equal and close singular values when specifying a threshold of such equality. Large breaks among the eigenvalues pairs in the eigenvalue spectra cannot lead to produce a harmonic component [40,44].…”

Section: The Ssa Theorymentioning

confidence: 99%

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Mitigating Wind Induced Noise in Outdoor Microphone Signals Using a Singular Spectral Subspace Method

Eldwaik

2018

Technologies

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Abstract:Wind induced noise is one of the major concerns of outdoor acoustic signal acquisition. It affects many field measurement and audio recording scenarios. Filtering such noise is known to be difficult due to its broadband and time varying nature. In this paper, a new method to mitigate wind induced noise in microphone signals is developed. Instead of applying filtering techniques, wind induced noise is statistically separated from wanted signals in a singular spectral subspace. The paper is presented in the context of handling microphone signals acquired outdoor for acoustic sensing and environmental noise monitoring or soundscapes sampling. The method includes two complementary stages, namely decomposition and reconstruction. The first stage decomposes mixed signals in eigen-subspaces, selects and groups the principal components according to their contributions to wind noise and wanted signals in the singular spectrum domain. The second stage reconstructs the signals in the time domain, resulting in the separation of wind noise and wanted signals. Results show that microphone wind noise is separable in the singular spectrum domain evidenced by the weighted correlation. The new method might be generalized to other outdoor sound acquisition applications.

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Section: Overviewmentioning

confidence: 99%