About the dataset

You can download the Barbie Vs Puppy dataset from here.

Audio classification using spectrograms

Our everyday lives are full of various types of audio signals. Our brains are capable of distinguishing different audio signals from each other by default. But machines don’t have this capability. To learn audio classification, different approaches can be used. One of them is classification using spectrograms. Audio classification is an important task that is required for various applications like speech recognition, music genre classification, environmental sound analysis, forensic departments, and many more. In this article, we will explore the implementation guide for classifying audio signals using Spectrogram.

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What is a spectrogram?

A spectrogram is a visual 2D representation of audio signals in the frequency domain that displays how the frequencies within a sound evolve over time by breaking down an audio signal into small segments and computing the intensity of different frequency components within each segment. The spectrogram, or time-frequency representation of an audio signal, helps us to understand valuable insights about the audio content, like distinguishing between various sounds, patterns, or characteristics. The efficient creation of spectrograms is a key step in audio classification using spectrograms. This spectrogram creation process involves various steps, which are discussed below....

About the dataset

You can download the Barbie Vs Puppy dataset from here....

Step-by-step implementation

Importing required libraries...

Conclusion

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