Audio Transfer Learning with Scikit-learn and Tensorflow

We present a didactic toolkit to rapidly prototype audio classifiers with pre-trained Tensorlow models and Scikit-learn. We use pre-trained Tensorflow models as audio feature extractors, and Scikit-learn classifiers are employed to rapidly prototype competent audio classifiers that can be trained on a CPU.

Check it on Github!

This material was prepared for teaching Tensorflow, Scikit-learn, and deep learning in general. Besides, due to the simplicity of Scikit-learn, this toolkit can be employed to easily build proof-of-concept models with your own data.



Slides: Training neural audio classifiers with few data

During the last summer, I have been a research intern at Telefónica Research (Barcelona). The article “Training neural audio classifiers with few data” is the outcome of this short (but intense!) collaboration with Joan Serrà, where we explored how to train deep learning models with just 1, 2 or 10 audios per class. Check it out on arXiv, and reproduce our results running our code! These slides are the extended version of what I will be presenting next week in ICASSP! See you in Brighton 🙂

Download the slides!

What’s up with waveform-based VGGs?

In this series of posts I have written a couple of articles discussing the pros & cons of spectrogram-based VGG architectures, to think about which is the role of the computer vision deep learning architectures in the audio field. Now is time to discuss what’s up with waveform-based VGGs!

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