Transfer files from university repo

I completed this task on a GitHub classroom repository. I am transferring the files to my personal repo so that I can make them public.

AudioConversionTool/mp3-signal-analysis.py

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+######################################################################################################################################
+# A script to analyse the frequencies and amplitudes of the sounds within an mp3 file across time
+# Takes an mp3 file as input and outputs a csv file
+# csv contains = frequencies in the columns (Hz), a row for each time increment (Secs) with amplitude (dB) values
+# A spectrogram plot will also be shown if requested
+######################################################################################################################################
+
+# imports from the pydub library
+from pydub import AudioSegment
+from pydub.utils import get_array_type
+
+# imports signal analysis function from scipy library
+from scipy import signal
+
+# imports data handling types - arrays, numpy arrays and pandas data frames
+import array
+import numpy as np
+import pandas as pd
+
+# imports the pyplot
+import matplotlib.pyplot as plt
+
+# imports the argument parser library
+import argparse
+
+def main():
+
+ show_spectrogram = False
+
+ # configure the argument parser, the -i and -o parameters are mandatory
+ cliparser = argparse.ArgumentParser(description="mp3-signal-analysis")
+ cliparser.add_argument("--i",help="the filepath/filename of the mp3 file to analyse", required='store_true')
+ cliparser.add_argument("--o",help="the filepath/filename of the csv output file", required='store_true')
+ cliparser.add_argument("--show", help="shows the analysis as a spectrogram", action='store_true')
+
+ # parse the arguments that have passed in
+ args = cliparser.parse_args()
+
+ # set the input file location using the -i parameter that was passed in on script start
+ input_file = args.i
+
+ # set the output csv file location using the -o parameter that was passed in on script start
+ output_file = args.o
+
+ # set whether the spectrogram will be shown(1) or not shown(0) - default is not shown
+ if args.show:
+ show_spectrogram = True
+
+ # load the mp3 file into an AudioSegment
+ # then split to mono and obtain the left audio channel
+ audio = AudioSegment.from_file(input_file)
+ left_channel = audio.split_to_mono()[0]
+
+ # perform some array conversions 
+ # first copy the original left channel data into a new array
+ # using the sample width of the audio * 8 bits as the array's data type
+ # then convert to a numpy array so that it can be used with the scipy spectrogram
+ bit_depth = left_channel.sample_width * 8
+ array_type = get_array_type(bit_depth)
+ signal_array = array.array(array_type, left_channel._data)
+ signal_np_array = np.array(signal_array) 
+
+ # use a scipy signal spectrogram to compute consecutive fourier transforms
+ # for the whole audio track using the frame rate of the audio as the sampling frequency
+ # leave the FFT windowing settings as default
+ freqs, times, amplitudes = signal.spectrogram(signal_np_array,left_channel.frame_rate)
+
+ # convert the amplitudes into decibels
+ amplitudes = 20 * np.log10(amplitudes)
+
+ # create a new pandas data frame to hold the output using
+ # time for the rows, frequency in Hz as the columns, and amplitudes as the cell values # 
+ # note: the amplitude array needs to be transposed inorder to swap the rows and columns around
+ # as the scipy spectrogram arranges data with rows for each frequency rather than each time increment 
+ df = pd.DataFrame(amplitudes.transpose(),
+ index=times,
+ columns=freqs)
+
+ # insert a new column at the beginning to hold the values
+ # of the time increments outputed by the spectrogram
+ df.insert(loc = 0, 
+ column = 'Time in Sec', 
+ value = times)
+
+ # save the data frame into the csv file
+ df.to_csv(output_file, index=False)
+
+ # optionally show the spectrogram if requested
+ if (show_spectrogram == True):
+ plt.pcolormesh(times, freqs, amplitudes)
+ plt.xlabel("Time in Seconds")
+ plt.ylabel("Frequency in Hz")
+ plt.show()
+
+# run the main program
+main()

Coursework2.sln

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