How to deal with audio with different length in Audio Classification

1. Which algorithm is best for audio classification?
2. How do you classify audio data?
3. What is audio feature extraction?
4. What is a mel spectrogram?

Which algorithm is best for audio classification?

Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio.

How do you classify audio data?

Audio classifications can be of multiple types and forms such as — Acoustic Data Classification or acoustic event detection, Music classification, Natural Language Classification, and Environmental Sound Classification. In this article, we will explore audio classification through a detailed hands-on project.

What is audio feature extraction?

Audio feature extraction is a necessary step in audio signal processing, which is a subfield of signal processing. It deals with the processing or manipulation of audio signals. It removes unwanted noise and balances the time-frequency ranges by converting digital and analog signals.

What is 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.