问如何使用傅里叶逆变换的输出？

EN

# General parameters
#
total_samples = length(time_or) # Total numbers of samples in the current series
max_time = max(time_or) # Length of the measurement in microseconds
sampling_freq = 1/(max_time/1000000)*total_samples # Sampling frequency
frame_length_t = 2 # In microseconds (time)
frame_length_s = round(frame_length_t/1000000*sampling_freq) # In samples per frame
overlap = frame_length_s/2 # Overlap in number of frames, set to 50% overlap
#
# Transform the frame to frequency domain
#
fft_frames = specgram(amp, n=frame_length_s, Fs=125, window=hamming(frame_length_s), overlap=overlap)

mag_spec=abs(fft_frames[["S"]])
phase_spec=atan(Im(fft_frames[["S"]])/Re(fft_frames[["S"]]))

#
# Determine the arrival time of noise
#
cutoff= 10 #determine the percentage of the signal that has to be cut off
dnr=us_data[(length(us_data[,1])*(cutoff/100)):length(us_data[,1]), ]
noise_arr=(length(us_data[,1])-length(dnr[,1])+min(which(dnr[,2]>0.01)))*0.008
#
# Select the frames for noise spectrum estimation
#
noise_spec=0
noise_spec=mag_spec[,noise_arr]
noise_spec=noise_spec+mag_spec[, (noise_arr+1)]
noise_spec=noise_spec+mag_spec[, (noise_arr+2)]
noise_spec_check=noise_spec/3
#
# Subtract the estimated noise spectrum from every frame
#
est_mag_spec=mag_spec-noise_spec_check
est_mag_spec[est_mag_spec < 0] = 0
#
# Transform back to frequency spectrum
#
j=complex(real=0, imaginary=1)
enh_spec = est_mag_spec*exp(j*phase_spec)
#
# Transform back to time domain
#
install.packages("pracma")
library("pracma")
enh_time=fft(enh_spec[,2], inverse=TRUE)