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Created 4 years ago
The purpose of creating this kernel is to provide - not only a step by step guide on how to convert a given audio clip to spectrogram which will be useful for various other audio analysis but also to explain what each step in audio loading and visualiztion is doing.
Provided some links in reference section at the end of the kernel.
** More information to be added
Step-1: Let's import all the required libraries
import os
import matplotlib.pyplot as plt
#for loading and visualizing audio files
import librosa
import librosa.display
#to play audio
import IPython.display as ipd
audio_fpath = "../input/audio/audio/"
audio_clips = os.listdir(audio_fpath)
print("No. of .wav files in audio folder = ",len(audio_clips))No. of .wav files in audio folder = 2002
Some information about audio data before we start with audio data processing
What are x and y axis in a audio wave representation?
- The y-axis represents sound pressure, the x-axis represents time.
Standard waveforms
Sine waveform
Square waveform
Rectangular waveform
Triangular waveform
Sawtooth waveform
** More info will be added here