window

Functions

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

    • 0 none

    • 1 power

    • 2 magnitude

• -w int

  • window type

    • 0 Blackman

    • 1 Hamming

    • 2 Hanning

    • 3 Bartlett

    • 4 Trapezoidal

    • 5 Rectangular

• 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

frame

class 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

enum NormalizationType

Normalization type.

Values:

enumerator kNone

enumerator kPower

enumerator kMagnitude

enumerator kNumNormalizationTypes

Public Functions

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.

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.