I need to gain volume to specific frequency (or range of frequencies). Closest I get is using audiochebband with band-reject. Sample pipeline would be:
gst-launch-1.0 audiotestsrc wave=9 samplesperbuffer=44100 num-buffers=30 volume=0.05 ! \
audio/x-raw,channels=2 ! audioconvert ! \
audiochebband lower-frequency=980 upper-frequency=1000 mode="band-reject" type=1 ripple=60 ! \
vorbisenc ! oggmux ! filesink location=amp.ogg
It gives me some 'some' results. After looking at the plot spectrum, there is huge cut-off after initial amplify of the given frequencies.
I need result like if you use , let say, equilizer inside audacidy, select points between two frequencies and move points up, like this.
Edit:
As an illustration of the requirements I have, here is the simple python script that reads wav file, and amplify range of frequencies.
from __future__ import print_function, division
import wave
import numpy as np
import time
start_time = time.time()
wr = wave.open('input-file.wav', 'r')
par = list(wr.getparams()) # Get the parameters from the input.
# This file is stereo, 2 bytes/sample, 44.1 kHz.
par[3] = 0 # The number of samples will be set by writeframes.
# Open the output file
ww = wave.open('output-file.wav', 'w')
ww.setparams(tuple(par)) # Use the same parameters as the input file.
lowcut = 980 # Select frequencies from.
highcut = 1000 # Select frequencies to.
sz = wr.getframerate() # Read and process 1 second at a time.
c = int(wr.getnframes()/sz) # whole file
for num in range(c):
print('Processing {}/{} s'.format(num+1, c))
da = np.frombuffer(wr.readframes(sz), dtype=np.int16)
left, right = da[0::2], da[1::2] # left and right channel
lf, rf = np.fft.rfft(left), np.fft.rfft(right)
lf[lowcut:highcut] *= 2
rf[lowcut:highcut] *= 2
nl, nr = np.fft.irfft(lf), np.fft.irfft(rf)
ns = np.column_stack((nl,nr)).ravel().astype(np.int16)
ww.writeframes(ns.tostring())
# Close the files.
wr.close()
ww.close()
print("--- %s seconds ---" % (time.time() - start_time))
Generating with noise, and use it as input file with script, there is a perfect gain in selected rage.
Yes. It must be user configurable. It can range from one to n range.
Using this method for one or two frequencies, the result is ok because
by the docs "some ferquencies may be amplified", but with wider range
it amplifies only couple of first and last. I know about
equilizer-nbands (or 3,10...) but I can't fine tune which to amplify
because all band range is amplified that way.
The following is just a suggestion (I can't test as I'm not set up), but as you are successful with one or two frequencies, perhaps you use multiple invocations to amplify subsets of your intending frequencies into different outputs / streams, and then mix the output into a single stream / file.
For example, say you need to amplify 980-2000. Lets start with what you say works:
gst-launch-1.0 audiotestsrc wave=9 samplesperbuffer=44100 num-buffers=30 volume=0.05 ! \
audio/x-raw,channels=2 ! audioconvert ! \
audiochebband lower-frequency=980 upper-frequency=1000 mode="band-reject" type=1 ripple=60 ! \
vorbisenc ! oggmux ! filesink location=amp.ogg
In the above example you are specifying 980-1000.
You can have separate invocations for:
1000-1020
1020-1040
....
1980-2000
All outputting to different files.
Then you can use something like the following to mix several streams into one by adding the data:
https://gstreamer.freedesktop.org/documentation/audiomixer/audiomixer.html?gi-language=c
or:
https://gstreamer.freedesktop.org/documentation/adder/index.html?gi-language=c
Related
I have an audio file and I want to split it every 2 seconds. Is there a way to do this with librosa?
So if I had a 60 seconds file, I would split it into 30 two second files.
librosa is first and foremost a library for audio analysis, not audio synthesis or processing. The support for writing simple audio files is given (see here), but it is also stated there:
This function is deprecated in librosa 0.7.0. It will be removed in 0.8. Usage of write_wav should be replaced by soundfile.write.
Given this information, I'd rather use a tool like sox to split audio files.
From "Split mp3 file to TIME sec each using SoX":
You can run SoX like this:
sox file_in.mp3 file_out.mp3 trim 0 2 : newfile : restart
It will create a series of files with a 2-second chunk of the audio each.
If you'd rather stay within Python, you might want to use pysox for the job.
You can split your file using librosa running the following code. I have added comments necessary so that you understand the steps carried out.
# First load the file
audio, sr = librosa.load(file_name)
# Get number of samples for 2 seconds; replace 2 by any number
buffer = 2 * sr
samples_total = len(audio)
samples_wrote = 0
counter = 1
while samples_wrote < samples_total:
#check if the buffer is not exceeding total samples
if buffer > (samples_total - samples_wrote):
buffer = samples_total - samples_wrote
block = audio[samples_wrote : (samples_wrote + buffer)]
out_filename = "split_" + str(counter) + "_" + file_name
# Write 2 second segment
librosa.output.write_wav(out_filename, block, sr)
counter += 1
samples_wrote += buffer
[Update]
librosa.output.write_wav() has been removed from librosa, so now we have to use soundfile.write()
Import required library
import soundfile as sf
replace
librosa.output.write_wav(out_filename, block, sr)
with
sf.write(out_filename, block, sr)
Suppose I have an object Trace, say trace, already and I want to have an amplitude data at the time 30 sec.
I think I can do it like below, assuming begin time is 0 for simplicity,
delta = trace.stats.delta
count = int(30 / delta)
target_value = trace.data[count]
Do they prepare a good way to do it?
something like....
trace.foo(time=30)
Right now we do not have such a convenience method, but I agree that it would be a good addition. You could make a feature request in our GitHub issue tracker: https://github.com/obspy/obspy/issues/new
In the meantime, you could make use of the sample times convenience functions mixed with some numpy to achieve what you want..
If you're looking for times relative to start of trace:
from obspy import read
tr = read()[0]
my_time = 4 # 4 seconds after start of trace
times = tr.times()
index = times.searchsorted(my_time)
print(times[index])
print(tr.data[index])
4.0
204.817965896
If you're looking for absolute times:
from obspy import read, UTCDateTime
tr = read()[0]
my_time = UTCDateTime("2009-08-24T00:20:12.0Z") # 4 seconds after start of trace
times = tr.times('utcdatetime')
index = times.searchsorted(my_time)
print(times[index])
print(tr.data[index])
2009-08-24T00:20:12.000000Z
156.68994731
If you're not matching the exact time of one sample, you will have to manually interpolate.
Hope this gets you started.
I was just playing around with sound input and output on a raspberry pi using python.
My plan was to read the input of a microphone, manipulate it and playback the manipulated audio. At the moment I tried to read and playback the audio.
The reading seems to work, since i wrote the read data into a wave file in the last step, and the wave file seemed fine.
But the playback is noise sounds only.
Playing the wave file worked as well, so the headset is fine.
I think maybe I got some problem in my settings or the output format.
The code:
import alsaaudio as audio
import time
import audioop
#Input & Output Settings
periodsize = 1024
audioformat = audio.PCM_FORMAT_FLOAT_LE
channels = 16
framerate=8000
#Input Device
inp = audio.PCM(audio.PCM_CAPTURE,audio.PCM_NONBLOCK,device='hw:1,0')
inp.setchannels(channels)
inp.setrate(framerate)
inp.setformat(audioformat)
inp.setperiodsize(periodsize)
#Output Device
out = audio.PCM(audio.PCM_PLAYBACK,device='hw:0,0')
out.setchannels(channels)
out.setrate(framerate)
out.setformat(audioformat)
out.setperiodsize(periodsize)
#Reading the Input
allData = bytearray()
count = 0
while True:
#reading the input into one long bytearray
l,data = inp.read()
for b in data:
allData.append(b)
#Just an ending condition
count += 1
if count == 4000:
break
time.sleep(.001)
#splitting the bytearray into period sized chunks
list1 = [allData[i:i+periodsize] for i in range(0, len(allData), periodsize)]
#Writing the output
for arr in list1:
# I tested writing the arr's to a wave file at this point
# and the wave file was fine
out.write(arr)
Edit: Maybe I should mention, that I am using python 3
I just found the answer. audioformat = audio.PCM_FORMAT_FLOAT_LE this format isn't the one used by my Headset (just copied and pasted it without a second thought).
I found out about my microphones format (and additional information) by running speaker-test in the console.
Since my speakers format is S16_LE the code works fine with audioformat = audio.PCM_FORMAT_S16_LE
consider using plughw (alsa subsystem supporting resampling/conversion) for the sink part of the chain at least:
#Output Device
out = audio.PCM(audio.PCM_PLAYBACK,device='plughw:0,0')
this should help to negotiate sampling rate as well as the data format.
periodsize is better to estimate based on 1/times of the sample rate like:
periodsize = framerate / 8 (8 = times for 8000 KHz sampling rate)
and sleeptime is better to estimate as a half of the time necessary to play periodsize:
sleeptime = 1.0 / 16 (1.0 - is a second, 16 = 2*times for 8000 KHz sampling rate)
`What is the best way to analyze a 2GB WAV file (1khz Tone) for audio dropouts using wave module? I tried the script below
import wave
file1 = wave.open("testdropout.wav", "r")
file2 = open("silence.log", "w")
for i in xrange(file1.getnframes()):
frame = file1.readframes(i)
zero = True
for j in xrange(len(frame)):
# check if amplitude is greater than 0
# the ord() function converts the hex values to integers
if ord(frame[j]) > 0:
zero = False
break
if zero:
print >> file2, 'dropout at second %s' % (file1.tell()/file1.getframerate())
file1.close()
file2.close()
I haven't used the wave module before, but file1.readframes(i) looks like it's reading 1 frame when you're at the first frame, 2 frames when you're at the second frame, 10 frames when you're in the tenth frame, and a 2Gb CD quality file might have a million frames - by the time you're at frame 100,000 reading 100,000 frames ... getting slower each time through the loop as well?
And from my comment, in Python 2 range() generates an in-memory array of the full size first, and xrange() doesn't, but not using range at all helps even more.
And push the looping down into the lower layers with any() to make the code shorter, and possibly faster:
import wave
file1 = wave.open("testdropout.wav", "r")
file2 = open("silence.log", "w")
chunksize = file1.getframerate()
chunk = file1.readframes(chunksize)
while chunk:
if not any(ord(sample) for sample in chunk):
print >> file2, 'dropout at second %s' % (file1.tell()/chunksize)
chunk = file1.readframes(chunksize)
file1.close()
file2.close()
This should read the file in 1-second chunks.
I think a simple solution to this would be to consider that the frame rate on audio files is pretty high. A sample file on my computer happens to have a framerate of 8,000. That means for every second of audio, I have 8,000 samples. If you have missing audio, I'm sure it will exist across multiple frames within a second, so you can essentially reduce your comparisons as drastically as your standards would allow. If I were you, I would try iterating over every 1,000th sample instead of every single sample in the audio file. That basically means it will examine every 1/8th of a second of audio to see if it's dead. Not as precise, but hopefully it will get the job done.
import wave
file1 = wave.open("testdropout.wav", "r")
file2 = open("silence.log", "w")
for i in range(file1.getnframes()):
frame = file1.readframes(i)
zero = True
for j in range(0, len(frame), 1000):
# check if amplitude is greater than 0
# the ord() function converts the hex values to integers
if ord(frame[j]) > 0:
zero = False
break
if zero:
print >> file2, 'dropout at second %s' % (file1.tell()/file1.getframerate())
file1.close()
file2.close()
At the moment, you're reading the entire file into memory, which is not ideal. If you look at the methods available for a "Wave_read" object, one of them is setpos(pos), which sets the position of the file pointer to pos. If you update this position, you should be able to only keep the frame you want in memory at any given time, preventing errors. Below is a rough outline:
import wave
file1 = wave.open("testdropout.wav", "r")
file2 = open("silence.log", "w")
def scan_frame(frame):
for j in range(len(frame)):
# check if amplitude is less than 0
# It makes more sense here to check for the desired case (low amplitude)
# rather than breaking at higher amplitudes
if ord(frame[j]) <= 0:
return True
for i in range(file1.getnframes()):
frame = file1.readframes(1) # only read the frame at the current file position
zero = scan_frame(frame)
if zero:
print >> file2, 'dropout at second %s' % (file1.tell()/file1.getframerate())
pos = file1.tell() # States current file position
file1.setpos(pos + len(frame)) # or pos + 1, or whatever a single unit in a wave
# file is, I'm not entirely sure
file1.close()
file2.close()
Hope this can help!
This question may be a little specialist, but hopefully someone might be able to help. I normally use IDL, but for developing a pipeline I'm looking to use python to improve running times.
My fits file handling setup is as follows:
import numpy as numpy
from astropy.io import fits
#Directory: /Users/UCL_Astronomy/Documents/UCL/PHASG199/M33_UVOT_sum/UVOTIMSUM/M33_sum_epoch1_um2_norm.img
with fits.open('...') as ima_norm_um2:
#Open UVOTIMSUM file once and close it after extracting the relevant values:
ima_norm_um2_hdr = ima_norm_um2[0].header
ima_norm_um2_data = ima_norm_um2[0].data
#Individual dimensions for number of x pixels and number of y pixels:
nxpix_um2_ext1 = ima_norm_um2_hdr['NAXIS1']
nypix_um2_ext1 = ima_norm_um2_hdr['NAXIS2']
#Compute the size of the images (you can also do this manually rather than calling these keywords from the header):
#Call the header and data from the UVOTIMSUM file with the relevant keyword extensions:
corrfact_um2_ext1 = numpy.zeros((ima_norm_um2_hdr['NAXIS2'], ima_norm_um2_hdr['NAXIS1']))
coincorr_um2_ext1 = numpy.zeros((ima_norm_um2_hdr['NAXIS2'], ima_norm_um2_hdr['NAXIS1']))
#Check that the dimensions are all the same:
print(corrfact_um2_ext1.shape)
print(coincorr_um2_ext1.shape)
print(ima_norm_um2_data.shape)
# Make a new image file to save the correction factors:
hdu_corrfact = fits.PrimaryHDU(corrfact_um2_ext1, header=ima_norm_um2_hdr)
fits.HDUList([hdu_corrfact]).writeto('.../M33_sum_epoch1_um2_corrfact.img')
# Make a new image file to save the corrected image to:
hdu_coincorr = fits.PrimaryHDU(coincorr_um2_ext1, header=ima_norm_um2_hdr)
fits.HDUList([hdu_coincorr]).writeto('.../M33_sum_epoch1_um2_coincorr.img')
I'm looking to then apply the following corrections:
# Define the variables from Poole et al. (2008) "Photometric calibration of the Swift ultraviolet/optical telescope":
alpha = 0.9842000
ft = 0.0110329
a1 = 0.0658568
a2 = -0.0907142
a3 = 0.0285951
a4 = 0.0308063
for i in range(nxpix_um2_ext1 - 1): #do begin
for j in range(nypix_um2_ext1 - 1): #do begin
if (numpy.less_equal(i, 4) | numpy.greater_equal(i, nxpix_um2_ext1-4) | numpy.less_equal(j, 4) | numpy.greater_equal(j, nxpix_um2_ext1-4)): #then begin
#UVM2
corrfact_um2_ext1[i,j] == 0
coincorr_um2_ext1[i,j] == 0
else:
xpixmin = i-4
xpixmax = i+4
ypixmin = j-4
ypixmax = j+4
#UVM2
ima_UVM2sum = total(ima_norm_um2[xpixmin:xpixmax,ypixmin:ypixmax])
xvec_UVM2 = ft*ima_UVM2sum
fxvec_UVM2 = 1 + (a1*xvec_UVM2) + (a2*xvec_UVM2*xvec_UVM2) + (a3*xvec_UVM2*xvec_UVM2*xvec_UVM2) + (a4*xvec_UVM2*xvec_UVM2*xvec_UVM2*xvec_UVM2)
Ctheory_UVM2 = - alog(1-(alpha*ima_UVM2sum*ft))/(alpha*ft)
corrfact_um2_ext1[i,j] = Ctheory_UVM2*(fxvec_UVM2/ima_UVM2sum)
coincorr_um2_ext1[i,j] = corrfact_um2_ext1[i,j]*ima_sk_um2[i,j]
The above snippet is where it is messing up, as I have a mixture of IDL syntax and python syntax. I'm just not sure how to convert certain aspects of IDL to python. For example, the ima_UVM2sum = total(ima_norm_um2[xpixmin:xpixmax,ypixmin:ypixmax]) I'm not quite sure how to handle.
I'm also missing the part where it will update the correction factor and coincidence correction image files, I would say. If anyone could have the patience to go over it with a fine tooth comb and suggest the neccessary changes I need that would be excellent.
The original normalised image can be downloaded here: Replace ... in above code with this file
One very important thing about numpy is that it does every mathematical or comparison function on an element-basis. So you probably don't need to loop through the arrays.
So maybe start where you convolve your image with a sum-filter. This can be done for 2D images by astropy.convolution.convolve or scipy.ndimage.filters.uniform_filter
I'm not sure what you want but I think you want a 9x9 sum-filter that would be realized by
from scipy.ndimage.filters import uniform_filter
ima_UVM2sum = uniform_filter(ima_norm_um2_data, size=9)
since you want to discard any pixel that are at the borders (4 pixel) you can simply slice them away:
ima_UVM2sum_valid = ima_UVM2sum[4:-4,4:-4]
This ignores the first and last 4 rows and the first and last 4 columns (last is realized by making the stop value negative)
now you want to calculate the corrections:
xvec_UVM2 = ft*ima_UVM2sum_valid
fxvec_UVM2 = 1 + (a1*xvec_UVM2) + (a2*xvec_UVM2**2) + (a3*xvec_UVM2**3) + (a4*xvec_UVM2**4)
Ctheory_UVM2 = - np.alog(1-(alpha*ima_UVM2sum_valid*ft))/(alpha*ft)
these are all arrays so you still do not need to loop.
But then you want to fill your two images. Be careful because the correction is smaller (we inored the first and last rows/columns) so you have to take the same region in the correction images:
corrfact_um2_ext1[4:-4,4:-4] = Ctheory_UVM2*(fxvec_UVM2/ima_UVM2sum_valid)
coincorr_um2_ext1[4:-4,4:-4] = corrfact_um2_ext1[4:-4,4:-4] *ima_sk_um2
still no loop just using numpys mathematical functions. This means it is much faster (MUCH FASTER!) and does the same.
Maybe I have forgotten some slicing and that would yield a Not broadcastable error if so please report back.
Just a note about your loop: Python's first axis is the second axis in FITS and the second axis is the first FITS axis. So if you need to loop over the axis bear that in mind so you don't end up with IndexErrors or unexpected results.