Three time-series graphs with three headings off to the left of each graph. The first heading is "Time Domain Waveform" and the graph shows a typical waveform. The two underneath are "Spectrogram" and "MFCC Spectrogram" and show two graphs in a range of green, yellow, orange and red colours

MFCC?! Yuh, Mel-frequency cepstral coefficients – obvz! Errrm, what?! Listen, they may be the secret sauce in my Doom and Tak project 🙊Could they be key to my AI-powered bellydancing robot? Could they help me bring this project to life and solve my woes? Maybe!

So, what makes MFCCs so special for analysing rhythmic sounds? Well, it all starts with how they mimic our human hearing. MFCCs are like the ears of AI models, capturing the way we perceive sound by focusing on the most crucial frequencies in vocal sounds. This means they MIGHT be brilliant at picking up the nuances in my “Doom” and “Tak” Egyptian rhythm sounds, making sure the robot dances perfectly to the beat.

Rhythmic sounds, as you know, are all about those delightful patterns and beats. MFCCs don’t just capture the sound; they capture the essence of the rhythm. By breaking down the audio into tiny, manageable bits, they allow the AI to understand the energy and timing in each beat of “Doom” and “Tak.” Sounds like it might be exactly what I need!

Another reason MFCCs are the thing I’m experimenting with at the moment is their efficiency. They condense all the important information from the audio into a compact form, making it easier and faster for the AI to process. This means quicker training times and more responsive performance, which is exactly what you need when you’re trying to get a robot to dance along!

One of the biggest challenges in audio analysis is dealing with background noise and variations in loudness. Thankfully, MFCCs seem to be potentially robust against these issues. Theoretically, they should maintain their accuracy even when there’s a bit of chaos in the background.

Stay tuned while I investigate! 👀

Over and Out 🫡

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