I have been working on heart sound signal in Matlab. Now I want to switch to Python but the problem with me is how to process two or more audio signals at a time. Thanks In advance
I have just started to work on data in the form of audio. I am using librosa as a tool. My project requires me to extract features like:
Total duration of the audio
Minimum Intensity of the audio signal
Maximum Intensity of the audio signal
Mean Intensity of the audio signal
Jitter
Rate of speaking
Number of Pauses
Maximum Duration of Pauses
Average Duration of Pauses
Total Duration of Pauses
Although, I know about these terms but I have no idea how to extract these from an audio file. Are these inbuilt in some form in the librosa.feature variable? Or we need to manually calculate these? Can someone guide me how to proceed?
I know that this job can be performed using softwares like Praat, but I need to do it in python.
Praat can be used for spectral analysis (spectrograms), pitch
analysis, formant analysis, intensity analysis, jitter, shimmer, and
voice breaks.
I'm writing a code in Python to plot a graph. Upon giving the input the graph shall be start playing. Exactly as we see the ECG wave form playing in the ECG monitor. Upon providing the certain input the graph shall start playing as per the input value provided. Please help me regarding this...
Thanks in Advance....
Check out the rtgraph package. It can do real-time charts. It uses gtk/pygtk on Linux.
I'm trying to modify this example: https://svn.enthought.com/enthought/browser/Chaco/trunk/examples/advanced/spectrum.py. Unfortunately I have not been able to get it to scale. If I double the sampling rate, the graph lags from the sound input. I'd like to find out which part of the code is the bottleneck. I tried to use cProfile but didn't investigate very far.
I wrote the original version of spectrum.py, and I believe that the bottleneck is in the drawing, in particular the spectrogram plot. If you change the code to not draw every time it computes an FFT, it should keep up better.
How would I go about using Python to read the frequency peaks from a WAV PCM file and then be able to generate an image of it, for spectogram analysis?
I'm trying to make a program that allows you to read any audio file, converting it to WAV PCM, and then finding the peaks and frequency cutoffs.
Python's wave library will let you import the audio. After that, you can use numpy to take an FFT of the audio.
Then, matplotlib makes very nice charts and graphs - absolutely comparable to MATLAB.
It's old as dirt, but this article would probably get you started on almost exactly the problem you're describing (article in Python of course).
Loading WAV files is easy using audiolab:
from audiolab import wavread
signal, fs, enc = wavread('test.wav')
or for reading any general audio format and converting to WAV:
from audiolab import Sndfile
sound_file = Sndfile('test.w64', 'r')
signal = wave_file.read_frames(wave_file.nframes)
The spectrogram is built into PyLab:
from pylab import *
specgram(signal)
Specifically, it's part of matplotlib. Here's a better example.
from pylab import *
specgram(signal)
is the easiest. Also quite handy in this context:
subplot
But be warned: Matplotlib is very slow but it creates beautiful images. You should not use it for demanding animation, even less when you are dealing with 3D
If you need to convert from PCM format to integers, you'll want to use struct.unpack.