Audio Spectrogram In Python Using Librosa & Matplotlib | Audio Machine Learning For Beginners

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Hi, my name is Jan Wilczek. I am an audio programmer and a researcher. Welcome to WolfSound!

WolfSound's mission is to provide high-quality, informative, and entertaining videos, articles, and courses on how to process sound by exploring mathematics, applying algorithms, and creating code in various programming languages. Topics include sound synthesis, digital signal processing, programming languages for audio (C, C++, Python, Rust), and audio effects. I am also sharing tips on how to learn fast and effectively. Make sure to visit https://www.thewolfsound.com/.

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ABOUT THE VIDEO

In digital signal processing (DSP), machine learning, and deep learning we often need a representation of an audio signal in an image form. The closest we can get is via using a spectrogram: the magnitude of a short-time Fourier transform (STFT). In this tutorial, I'm showing you how to calculate the spectrogram, plot it, and save it.

From the video, you will learn:

* ✅ Which libraries to use
* ✅ How to effortlessly compute the STFT of an audio signal
* ✅ Step-by-step writing of the plot_spectrogram_and_save() function
* ✅ How to plot the spectrogram in decibels full-scale (dBFS)
* ✅ How to mark the frequency axis using the ISO-standardized octave band marks
* ✅ How to adjust the figure to your needs (colors, labels, font size, and more)
* ✅ How to export your figure to a .png file effortlessly

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ABOUT ME

My name is Jan Wilczek. I am an audio programmer, a researcher in the field of music technology, and a musician. Through articles and videos from WolfSound, you will easily understand the main concepts of sound processing using software. My full story:    • How I Got Started With Audio Programm...  

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TIME CODES

00:00 Introduction
00:55 Creating the virtual environment & installing necessary libraries
02:40 Reading example audio file
04:20 Calculating the spectrogram
06:20 Plotting the spectrogram
10:28 Styling the figure
13:45 Changing STFT parameters
16:00 Explanation of STFT parameters
19:05 Saving the figure to file
21:50 Free resource for learning DSP
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#audio #python #dsp