spectrum

问题 I am using spectrum (https://github.com/bgrins/spectrum), a jQuery plugin for color palletes. My goal is that the color is being directly applied on the background, while the user is browsing the different colors. What I have so far is this: //*****Part of a 3rd party file, which I cannot modify**** $("#picker1").spectrum({ allowEmpty:true, color: "#ECC", showInput: true, preferredFormat: "hex", }); //*********************************** $("#picker1").on("change keyup paste", function(){

问题 How to generate file including spectrum of frequencies of wav/riff sound file? I would like to use linux command line. I know the cool sox function to generate png spectrograms sox sound.wav -n spectrogram But I do not need visual representation of the spectrum of frequencies. I just want to get spectrum of frequencies in data file so I can work on them. I believe that there must be an option using sox. Sox need to generate that data before plotting it. How to get this? Not sure, maybe the

问题 The following code generates a spectrogram using either scipy.signal.spectrogram or matplotlib.pyplot.specgram . The color contrast of the specgram function is, however, rather low. Is there a way to increase it? import numpy as np from scipy import signal import matplotlib.pyplot as plt # Generate data fs = 10e3 N = 5e4 amp = 4 * np.sqrt(2) noise_power = 0.01 * fs / 2 time = np.arange(N) / float(fs) mod = 800*np.cos(2*np.pi*0.2*time) carrier = amp * np.sin(2*np.pi*time + mod) noise = np

问题 I already have the program of the player, wave form generator, spectrum analyzer, the list box where the time will be recorded. These stuffs are working. Now, I want to add the feature, when you will play the audio file, and it reached the certain threshold or the max peak of the spectrum, the time of that current event will be recorded to the list box. I managed to add time in the list box so now, my only problem is how to detect the event. I used FFT in the spectrum analyzer, but I;m stuck

问题 I have found for several times the following guidelines for getting the power spectrum of an audio signal: collect N samples, where N is a power of 2 apply a suitable window function to the samples, e.g. Hanning pass the windowed samples to an FFT routine - ideally you want a real-to-complex FFT but if all you have a is complex-to-complex FFT then pass 0 for all the imaginary input parts calculate the squared magnitude of your FFT output bins (re * re + im * im) (optional) calculate 10 *

问题 I have found for several times the following guidelines for getting the power spectrum of an audio signal: collect N samples, where N is a power of 2 apply a suitable window function to the samples, e.g. Hanning pass the windowed samples to an FFT routine - ideally you want a real-to-complex FFT but if all you have a is complex-to-complex FFT then pass 0 for all the imaginary input parts calculate the squared magnitude of your FFT output bins (re * re + im * im) (optional) calculate 10 *

问题 I have a signal spectrum PSD that looks like : The frequency range of the PSD is np.linspace(0,2,500). I want to convert this spectrum into a time series of 600s . The code is shown below: def spectrumToSeries(timeSeries,frequency,psdLoad): ''' Function that gicen a PSD converts into a time series ''' # #Obtian interval frequency df=frequency[2]-frequency[1] #Obtian the spectrum amplitudes amplitude=np.sqrt(2*np.array(psdLoad)*df) #Pre allocation of matrices epsilon=np.zeros((len(amplitude)))

问题 I know by theory that the energy spectrum of a given signal is the sum of the squared fourier coefficient . What if I have the real and imaginary part of the corresponding fourier coefficient, can I say that energy spectrum of a given signal is equal to sum of (real part + imaginary part)^2 I hope that is straightforward what Im trying to say?! best regards ben 回答1: Not quite. You want: sum of fft_result_magnitudes^2 which is: sum of (sqrt(real_part^2 + imaginary_part^2)^2 which is: sum of

问题 This question addresses in particular the question of curve fitting in the context of color mixing of paints, pigments, etc. I am trying to guess the required proportions of two paints, let's say "Brown" (B) and "white" (W) to get to a given lightness value L. I have made a "calibration curve" in the same fashion as one does so for applying the Beer-lambert law in chemistry. However, the curve is not linear so I cannot use the Beer-Lambert law. Here's what I've done : (1) I have measured the

问题 I'm trying to plot multiple spectra on the same plot with each spectrum having different colors. I use a 'hyperSpec' object from the hyperSpec package and ggplot2. My data set is pretty big, but a portion of it looks like this: > dataTable 1_6-5p.asc 1_6-25p.asc 1_6-50p.asc 1_6-75p.asc 1_6-95p.asc 4000 98.35901 97.04647 98.65234 99.17536 97.11173 3999 98.35578 97.04401 98.65169 99.17371 97.11437 3998 98.35255 97.03779 98.65102 99.17253 97.11699 3997 98.34935 97.03048 98.65038 99.17188 97