Boring Blackalicious

I recently took the MIR Workshop at CCRMA Stanford (which I highly recommend) and got a chance to play around with python signal processing libraries including librosa. During the week one of the guest presenters used ‘Alphabet Aerobics’ by Blackalicious to demonstrate his source separation algorithm. This was a challenging piece of material because this track famously does not have a constant tempo and speeds up considerably throughout the song.

The thought struck me that it’d be way less interesting if the tempo was constant throughout, so this weekend I put my newly developed python processing skills to work and created ‘Boring Blackalicious’ – the constant tempo version of Alphabet Aerobics.

It uses librosa’s onset detection, beat tracking and tempo estimation functions to create a tempo map (with the help of some manual tweaking to correct the estimated tempos of the later segments, where the librosa functions had more difficulty keeping up).

The tempo map was used to calculate the correct rate to slow down each segment using librosa’s phase vocoder function. I used the phase vocoder over librosa.effect.time_stretch so I could tweak the fft length directly to get a better sounding result.

The python source is available here and you can listen to the original for comparison below!

Compressed Sensing – An Introduction

This week Emmanuel Candès, professor of Mathematics and Statistics at Stanford University has been elected to the American Academy of Arts and Sciences. He is one of the authors (the other being Terence Tao) of the paper which brought about the field of compressed sensing. If you’ve ever done any signal processing, then you’ll know

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Extracting Audio From a Mix by Singing It – Source Separation via Input Matching

Paris Smaragdis is an assistant professor at the University of Illinois who specializes in research that involves machine listening. This includes source localization (where the sound is coming from), sound recognition (such as a traffic accident at an intersection) and source separation (taking individual voices or instruments out of a mix). Source separation is a

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