window
Functions
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int main(int argc, char *argv[])
window [ option ] [ infile ]
-l int
input length \((1 \le L_1)\)
-L int
output length \((1 \le L_2)\)
-n int
normalization type
0none1power2magnitude
-w int
window type
0Blackman1Hamming2Hanning3Bartlett4Trapezoidal5Rectangular
infile str
double-type data sequence
stdout
double-type windowed data sequence
The below example performs spectral analysis with Blackman window.
frame -l 400 -p 80 data.d | window -l 400 -L 512 | spec -l 512 > data.spec
- Parameters
argc – [in] Number of arguments.
argv – [in] Argument vector.
- Returns
0 on success, 1 on failure.
See also
-
class sptk::DataWindowing
Apply a window function.
The input is the \(L_1\)-length data:
\[ \begin{array}{cccc} x(0), & x(1), & \ldots, & x(L_1-1), \end{array} \]and the output is the \(L_2\)-th length data:\[ \begin{array}{cccc} x'(0), & x'(1), & \ldots, & x'(L_2-1). \end{array} \]where\[\begin{split} x'(l) = \left\{ \begin{array}{cc} x(l)w(l), & l < L_1 \\ 0. & L_1 \le l \end{array} \right. \end{split}\]and \(w(\cdot)\) is a window.The window \(w(\cdot)\) is normalized depending on the given normalization type. There are two types of normalization:
\[ \sum_{l=0}^{L_1} w^2(l) = 1 \]and\[ \sum_{l=0}^{L_1} w(l) = 1. \]Public Types
Public Functions
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DataWindowing(WindowInterface *window, int output_length, NormalizationType normalization_type)
- Parameters
window – [in] \(L_1\)-length window to be used.
output_length – [in] Output length, \(L_2\).
normalization_type – [in] Type of normalization.
-
inline int GetInputLength() const
- Returns
Input length.
-
inline int GetOutputLength() const
- Returns
Output length.
-
inline bool IsValid() const
- Returns
True if this object is valid.
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bool Run(const std::vector<double> &data, std::vector<double> *windowed_data) const
- Parameters
data – [in] \(L_1\)-length input data.
windowed_data – [out] \(L_2\)-length output data.
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DataWindowing(WindowInterface *window, int output_length, NormalizationType normalization_type)