librosa VS aubio

librosa/librosa: Python library for audio and music analysis

How does this compare to mature libraries for other platforms like librosa?

I use librosa now. What other useful audio feature extraction libraries are there?

librosa can do that easily but I think there is an easier way to find what are you looking for:

I have a .wav file, and using python, I'd like to get a list of every audio frame where the amplitude is at the resting position. How could I achieve this? I think the librosa library could do such a thing, but I'm struggling to find exactly how to do it. Any help would be greatly appreciated, thank you.

Librosa is a Python package developed for music and audio analysis. It is specific on capturing the audio information to be transformed into a data block. However, the documentation and example are good to understand how to work with audio data science projects.

Hello! welcome once again to the continuation of the last blog post about audio analysis using the Librosa python library, if you missed this article don't worry here you can enjoy audio analysis techniques with Librosa.

Changing pitch of the audio:- in this technique python package for audio analysis like Librosa is the best tool to go with, by adding effect on the audio pitch to create new audio data.

Aubio is a good library for working with audio and midi: https://aubio.org/

How does this compare to Aubio?

Beyond that you'll need pitch detection/pitch tracking. It's not the most difficult thing in the world but you need basic understanding of digital signal processing, and the math behind it. Otherwise I think this library can do the heavy lifting for you.

aubio looks like an option. It contains the console program aubiopitch with a “jack” option for live audio. HTH.

I dunno, that article is paywalled. ES-HyperNEAT uses what's referred to an an evolvable substrate, which is the internal architecture of the ANN being involved. This provides for arbitrary neuronal wiring which is interesting and a departure from conventional GPU-based training architectures based on layers. For classifying audio stuff typically spectrogram analysis with CNNs are used, to pick up on latent features from a visual perspective. Probably getting your audio sources into an appropriate format would be the challenging part of it. Maybe experiment with the output from Aubio to get it into a representable format that an ES-HyperNEAT ANN could consume.

Hey, glad that you like the idea. For the audio-analysis, I'm using aubio (https://aubio.org/), great library, once you managed to install it :D Was a bit tricky in my case.