signal-processing

问题 I am currently trying to compare a simple inverse filter to the wiener filter for deconvolution using matlab. My starting signal is exp(-t^2) and this is to be convolved with a rect that is nonzero for times -.5 to .5. I am introducing noise with amplitude in the range -.5 to .5. Defining my time domain to frequency domain mapping: f = exp(-t^2) => F s = rect => R c = f*s => C r = noise (see above) => R with noise c becomes: c = f*s + n => C = FxS + N For the first approach I am simply taking

问题 I have a simple EEG signal in MATLAB such as that shown in the figure below. And what I wanted was to extract the components of the EEG according to the following table. Delta - up to 4 Hz; Theta - 4 -> 8 Hz Alpha - 8 -> 13 Hz Beta - 13 -> 30 Hz Gamma - 30 -> 100 Hz In a first attempt to solve this problem I tried to build a bandpass filter with 'fdatool' from MATLAB to extract the component' theta 'signal, but without success. Along attached the code for the filter obtained with the 'fdatool

问题 I have a short (~1 second) arbitrary sound file and two devices. At some unknown time, device 1 will play the sound file out of its speaker. Device 2 should then be able to detect that sound. There may be background noise. It's unknown how loud the sound will be played. This feels like it should be a common solved problem, but searching for answers has left me with nothing. If anyone has good a solution or could just point me in the right direction I'd be very grateful. 回答1: In most distance

问题 I am trying to extract features from .wav files by using MFCC's of the sound files. I am getting an error when I try to convert my list of MFCC's to a numpy array. I am quite sure that this error is occurring because the list contains MFCC values with different shapes (But am unsure of how to solve the issue). I have looked at 2 other stackoverflow posts, however these don't solve my problem because they are too specific to a certain task. ValueError: could not broadcast input array from

问题 I am trying to use KNN to correctly classify .wav files into two groups, group 0 and group 1. I extracted the data, created the model, fit the model, however when I try and use the .predict() method I get the following error: Traceback (most recent call last): File "/..../....../KNN.py", line 20, in <module> classifier.fit(X_train, y_train) File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/neighbors/base.py", line 761, in fit X, y = check_X_y(X, y,

问题 I am trying to use KNN to correctly classify .wav files into two groups, group 0 and group 1. I extracted the data, created the model, fit the model, however when I try and use the .predict() method I get the following error: Traceback (most recent call last): File "/..../....../KNN.py", line 20, in <module> classifier.fit(X_train, y_train) File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/neighbors/base.py", line 761, in fit X, y = check_X_y(X, y,

问题 I am trying to get a third-octave frequency spectrum of a time signal. The time signal is the acoustic pressure of rotational rotor noise which is harmonic. Its fundamental frequency is ff = n * N_b and for that reason, all frequencies should be multiples of ff. Using fft I get the expected result: Multiples of the fundamental frequency are the relevant frequencies in the spectrum. To get the third-octave frequency spectrum I wanted to use python acoustics, but the result of the function

问题 I need to automatically detect dips in a 2D plot, like the regions marked with red circles in the figure below. I'm only interested in the "main" dips, meaning the dips have to span a minimum length in the x axis. The number of dips is unknown, i.e., different plots will contain different numbers of dips. Any ideas? Update: As requested, here's the sample data, together with an attempt to smooth it using median filtering, as suggested by vines. Looks like I need now a robust way to

问题 After researching and a lot of trials-and-errors, I have come to a point that I can construct a spectrogram which I think it has element of rights and wrongs . 1. First, I read .wav file into a byte array and extract only the data part. 2. I convert the byte array into a double array which takes the average of right and left channels. I also notice that 1 sample of 1 channel consists of 2 bytes. So, 4 bytes into 1 double. 3. For a certain window size of power of 2, I apply FFT from here and

问题 I am trying to convert few .wav files to .mp3 format The desired .mp3 format is : I tried with FFmpeg with this code : ffmpeg -i input.wav -vn -ac 2 -b:a 160k output1.mp3 This is the output of this command on one .wav format I am getting the result but two things are different Overall bit rate mode and Writing library Writing library: LAME3.99.5 vs LAME3.100 ( I think this shouldn't make any problem?) bit rate mode Constant Vs variable How to change bit rate mode from variable to Constant?