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This repository was archived by the owner on Sep 27, 2023. It is now read-only.

Another branched project. The labeler is more focuesed on the recording of the PCM while this project is more focused on the processing of the PCM: Fourier transform, random sampling with pseudo-random distribution, interpolating with psuedo-random distribution, truncating and elongating PCM data

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Mins0o/AudioSignalProcessing

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PCMProcessing.py

Truncate, Elongate data

  • TruncateToMinLength, ElongateToMaxLength
    These methods takes multiple .tsv files in and cuts or attaches tail to the .tsv file to match the length of the data. The attachment is just a linear approach towards the average of the data.

Match Frequency

From last project data of different sampling frequency was created. While facing small-data-problem in the ML experiment, I tried to find a way to combine the two data. Resampling data from a different sampling rate is not a good approach to solve the data shortage problem, but I was able to learn how to use a rather uniform distribution of pseudo-random occurences to make the sampling/interpolating less distorting to the original data.

PCMFeature.py

Fourier Transform

Fourier transform is a rather simple but extremely powerful mathematical tool to analyze audio data. Fourier transform decomposes the sound signal into frequency domain. A detailed method of how I performed the Fourier transform can be seen inside the code's comment.


Fourier transform, random sampling with uniform distribution, interpolating with pseudo-random distribution, truncating and elongating PCM data

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Another branched project. The labeler is more focuesed on the recording of the PCM while this project is more focused on the processing of the PCM: Fourier transform, random sampling with pseudo-random distribution, interpolating with psuedo-random distribution, truncating and elongating PCM data

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