Choosing the best measure for machine learning on a Spectrogram

1. What is spectrogram in machine learning?
2. How do you read a mel spectrogram?
3. How are spectrograms useful?

What is spectrogram in machine learning?

(Spectrograms are images of time-frequency domain features that were extracted from wave signals) And once you have those, then you can move forward with a straight ahead image classification deep learning project using those spectrograms.

How do you read a mel spectrogram?

A mel spectrogram logarithmically renders frequencies above a certain threshold (the corner frequency). For example, in the linearly scaled spectrogram, the vertical space between 1,000 and 2,000Hz is half of the vertical space between 2,000Hz and 4,000Hz.

How are spectrograms useful?

As a collection of time-frequency analyses, the spectrogram can be used to identify characteristics of nonstationary or nonlinear signals. For this reason, a spectrogram is a helpful tool for analyzing real-world data where there are various frequency components and/or mechanical and electrical noise.