Develop Reference

How do I change the speed of an audio file in Python, like in Audacity, without quality loss?

I'm building a simple Python application that involves altering the speed of an audio track.
(I acknowledge that changing the framerate of an audio also make pitch appear different, and I do not care about pitch of the audio being altered).
I have tried using solution from abhi krishnan using pydub, which looks like this.
from pydub import AudioSegment
sound = AudioSegment.from_file(…)
def speed_change(sound, speed=1.0):
# Manually override the frame_rate. This tells the computer how many
# samples to play per second
sound_with_altered_frame_rate = sound._spawn(sound.raw_data, overrides={
"frame_rate": int(sound.frame_rate * speed)
})
# convert the sound with altered frame rate to a standard frame rate
# so that regular playback programs will work right. They often only
# know how to play audio at standard frame rate (like 44.1k)
return sound_with_altered_frame_rate.set_frame_rate(sound.frame_rate)
However, the audio with changed speed sounds distorted, or crackled, which would not be heard with using Audacity to do the same, and I hope I find out a way to reproduce in Python how Audacity (or other digital audio editors) changes the speed of audio tracks.
I presume that the quality loss is caused by the original audio having low framerate, which is 8kHz, and that .set_frame_rate(sound.frame_rate) tries to sample points of the audio with altered speed in the original, low framerate. Simple attempts of setting the framerate of the original audio or the one with altered framerate, and the one that were to be exported didn't work out.
Is there a way in Pydub or in other Python modules that perform the task in the same way Audacity does?

Assuming what you want to do is to play audio back at say x1.5 the speed of the original. This is synonymous to saying to resample the audio samples down by 2/3rds and pretend that the sampling rate hasn't changed. Assuming this is what you are after, I suspect most DSP packages would support it (search audio resampling as the keyphrase).
You can try scipy.signal.resample_poly()
from scipy.signal import resample_poly
dec_data = resample_poly(sound.raw_data,up=2,down=3)
dec_data should have 2/3rds of the number of samples as the original raw_data samples. If you play dec_data samples at the sound's sampling rate, you should get a sped-up version. The downside of using resample_poly is you need a rational factor, and having large numerator or denominator will cause output less ideal. You can try scipy's resample function or seek other packages, which supports audio resampling.

band pass filter for removing noise in the set sound (collection of sound ) in python

how will we use a bandpass filter for removing noise in the set sound (collection of sound ) not only one sound but a set of sounds?? using a python programming language
I have a set of audio waves (example: lung sound signal). each sound has one .text extension. so, I want to remove noise from all audio and then I will connect audio with .text. and finally I will finish my work.
the main point I need is to remove noise through preprocessing step in deep learning. how will I do it?
please, help

There's a recipe on Scipy cookbook for a butterworth bandpass:
https://scipy-cookbook.readthedocs.io/items/ButterworthBandpass.html
You might be able to adapt that as your bandpass, but it sort of depends a bit on the frequencies you want to be able to filter out.
I'd say it would be easier to do your audio pre-processing in an audio specific programme, there are free ones out there like Audacity and then feed the processed data into your deep learning module. Good luck!

Break down each period of a signal

I have an experimental bench where I retrieve data of the power of a compressor.
I import the csv using python and pandas. So it's a pandas dataframe with index datetime and a float column with P_comp.
And I would like to define and calculate the area under the curve for each period like this :
For the moment, I do it manually which is really annoying, I'm plotting all the data and manually selecting a range where there is a periodic steady state and then I'm integrating P_comp using np.trapz on this range.
I tried scipy.signal but I’m not sure it’s a good tool to do this job. Do you have any ideas ?

Looks like the intervals are fairly regular and the low-values are almost equal, too, so you might get away with taking the first value below a defined threshold, and then after a period of time the next etc.

Thank you, I found the solution using scipy.signal.find_peaks and numpy diff

Remove spike noise from data in Python

I'm transitioning all of my data analysis from MATLAB to Python and I've finally hit a block where I've been unable to quickly find a turnkey solution. I have time series data from many instruments including an ADV (acoustic doppler velocimeter) that require despiking. Previously I've used this function in MATLAB that works quite well:
http://www.mathworks.com/matlabcentral/fileexchange/15361-despiking
Is anybody aware of a similar function available in Python?

I'd use median filter, and there are plenty of options depending on your data class, for example
import scipy.ndimage as im
x= im.median_filter(x, (self.m,self.m))

Making specific frequency (ranges) louder

I want to make certain frequencies in a sequence of audio data louder. I have already analyzed the data using FFT and have gotten a value for each audio frequency in the data. I just have no idea how I can use the frequencies to manipulate the sound data itself.
From what I understand so far, data is encoded in such a way that the difference between every two consecutive readings determines the audio amplitude at that time instant. So making the audio louder at that time instant would involve making the difference between the two consecutive readings greater. But how do I know which time instants are involved with which frequency? I don't know when the frequency starts appearing.
(I am using Python, specifically PyAudio for getting the audio data and Num/SciPy for the FFT, though this probably shouldn't be relevant.)

You are looking for a graphic equalizer. Some quick Googling turned up rbeq, which seems to be a plugin for Rhythmbox written in Python. I haven't looked through the code to see if the actual EQ part is written in Python or is just controlling something in the host, but I recommend looking through their source.