I have the following code: which is giving me the following plot: What is the correct way to define the correct parameters, namely: nperseg, nfft and noverlap to obtain a correct and smooth plot? Thank you! Plot of the input signal: Answer I was able to solve this issue by normalising the input signal and doing:
Tag: spectrogram
Librosa – Audio Spectrogram/Frequency Bins to Spectrum
I’ve read around for several days but haven’t been to find a solution… I’m able to build Librosa spectrograms and extract amplitude/frequency data using the following: However, I cannot turn the data in D and freq_bins back into a spectrum. Once I am able to do this I can convert the new spectrum into a .wav file and listen to
Python spectrogram in 3D (like matlab’s spectrogram function)
My question is the following: I have all the values that I need for a spectrogram (scipy.fftpack.fft). I would like to create a 3D spectrogram in python. In MATLAB this is a very simple task, while in python it seems much more complicated. I tried mayavi, 3D plotting matplotlib but I have not managed to do this. Thanks My code:
Matplotlib spectrogram animation without calling pyplot.specgram directly every cycle
I have made an animated spectrogram this way: It works, but in examples of using FuncAnimation I’ve seen, people don’t call the whole plotting function for every animation frame but update the data directly instead and it feels as if there are probably reasons (performance?) to do this. The examples gave some idea of how to do this for other
Why spectrogram from librosa library have twice the time duration of the actual audio track?
I am using the following code to obtain Mel spectrogram from a recorded audio signal of about 30 s: Obtained spectrogram: Mel spectrogram Can you please explain me why the time axis depicts twice the time duration (it should be 30 s). What is going wrong with the code? Answer You need to pass the sampling rate to librosa.display.specshow (sr=self.SamplingFrequency).
Scipy.signal.spectrogram output lengths
I am trying to analyze the frequencies of a song at certain points of time held inside an array. I am using the scipy.signal.spectrogram function to generate those frequencies. the length of the song is 2:44, or 164 seconds, and the sampling rate of the scipy.wavfile read is 44100. When I use spectrogram: The length of f is really small,