Week 5 – Improvements To Audio Alteration
This week, I found a suitable library for audio alteration and began adapting it for my own project.
Python has a library known as “noisereduce” which, as the name suggests, focuses on reducing noise from audio samples. Through a process called spectral gating, the algorithm computes a spectrogram of a signal and estimates a spread of frequencies (frequency of the noise in a sound). Based on that spread, it will construct a mask that dulls out the noise while preserving the main voice in a particular audio.
Now let’s go through this process with some code.
First, we read in the audio. This library works with .wav files, so for my final product, I will have to convert more common .mp3 files into .wav format.
This particular audio doesn’t have much noise, so for the sake of example, I’ll add some noise by overlapping a standard frequency over the sound.
Now, let’s use the library’s method to actually remove the noise. The method sets a set of ranges of frequencies to eliminate the squeezes the noise out of the sound. As you can see, the important sounds in the full audio are preserved.
Now that I know how to remove noise from audio files, I can actually start working on adapting it to my project. After testing this algorithm on other sounds, I will begin combining this model with my colorization model. This will be the final and most difficult stage of my project, so stay tuned to the upcoming posts!