gstreamer-fft-0.10

Description:

Content:

Namespaces:

Classes:

FFTF32 - FFTF32 provides a FFT implementation and related functions for 32 bit float samples. To use this call FFTF32 for allocating a FFTF32 instance with the appropiate parameters and then call fft or inverse_fft to perform the FFT or inverse FFT on a buffer of samples. After use free the FFTF32 instance with gst_fft_f32_free. For the best performance use fft_next_fast_length to get a number that is entirely a product of 2, 3 and 5 and use this as the The len parameter specifies the number of samples in the time domain that will be processed or generated. The number of samples in the frequency domain is len/2 + 1. To get n samples in the frequency domain use 2*n - 2 as len. Before performing the FFT on time domain data it usually makes sense to apply a window function to it. For this window can comfortably be used. Be aware, that you can't simply run inverse_fft on the resulting frequency data of fft to get the original data back. The relation between them is iFFT (FFT (x)) = x * nfft where nfft is the length of the FFT. This also has to be taken into account when calculation the magnitude of the frequency data.
FFTF32Complex - Data type for complex numbers composed of 32 bit float.
FFTF64 - FFTF64 provides a FFT implementation and related functions for 64 bit float samples. To use this call FFTF64 for allocating a FFTF64 instance with the appropiate parameters and then call fft or inverse_fft to perform the FFT or inverse FFT on a buffer of samples. After use free the FFTF64 instance with gst_fft_f64_free. For the best performance use fft_next_fast_length to get a number that is entirely a product of 2, 3 and 5 and use this as the The len parameter specifies the number of samples in the time domain that will be processed or generated. The number of samples in the frequency domain is len/2 + 1. To get n samples in the frequency domain use 2*n - 2 as len. Before performing the FFT on time domain data it usually makes sense to apply a window function to it. For this window can comfortably be used. Be aware, that you can't simply run inverse_fft on the resulting frequency data of fft to get the original data back. The relation between them is iFFT (FFT (x)) = x * nfft where nfft is the length of the FFT. This also has to be taken into account when calculation the magnitude of the frequency data.
FFTF64Complex - Data type for complex numbers composed of 64 bit float.
FFTS16 - FFTS16 provides a FFT implementation and related functions for signed 16 bit integer samples. To use this call FFTS16 for allocating a FFTS16 instance with the appropiate parameters and then call fft or inverse_fft to perform the FFT or inverse FFT on a buffer of samples. After use free the FFTS16 instance with gst_fft_s16_free. For the best performance use fft_next_fast_length to get a number that is entirely a product of 2, 3 and 5 and use this as the The len parameter specifies the number of samples in the time domain that will be processed or generated. The number of samples in the frequency domain is len/2 + 1. To get n samples in the frequency domain use 2*n - 2 as len. Before performing the FFT on time domain data it usually makes sense to apply a window function to it. For this window can comfortably be used. Be aware, that you can't simply run inverse_fft on the resulting frequency data of fft to get the original data back. The relation between them is iFFT (FFT (x)) = x / nfft where nfft is the length of the FFT. This also has to be taken into account when calculation the magnitude of the frequency data.
FFTS16Complex - Data type for complex numbers composed of signed 16 bit integers.
FFTS32 - FFTS32 provides a FFT implementation and related functions for signed 32 bit integer samples. To use this call FFTS32 for allocating a FFTS32 instance with the appropiate parameters and then call fft or inverse_fft to perform the FFT or inverse FFT on a buffer of samples. After use free the FFTS32 instance with gst_fft_s32_free. For the best performance use fft_next_fast_length to get a number that is entirely a product of 2, 3 and 5 and use this as the The len parameter specifies the number of samples in the time domain that will be processed or generated. The number of samples in the frequency domain is len/2 + 1. To get n samples in the frequency domain use 2*n - 2 as len. Before performing the FFT on time domain data it usually makes sense to apply a window function to it. For this window can comfortably be used. Be aware, that you can't simply run inverse_fft on the resulting frequency data of fft to get the original data back. The relation between them is iFFT (FFT (x)) = x / nfft where nfft is the length of the FFT. This also has to be taken into account when calculation the magnitude of the frequency data.
FFTS32Complex - Data type for complex numbers composed of signed 32 bit integers.

Enums:

FFTWindow - The various window functions available.

Methods:

public int fft_next_fast_length (int n)
Returns the next number to n that is entirely a product of 2, 3 and 5. Using this as the len parameter for the different GstFFT types will provide the best performance.