Upsamplers are a key element for developing computationally efficient and high-fidelity neural audio synthesizers. Given their importance, together with the fact that the audio literature only provides sparse and unorganized insights, our work is aimed at advancing and consolidating our current understanding of neural upsamplers.
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SESQA: Semi-supervised Learning for Speech Quality Assessment
Can semi-supervised learning help us estimating accurate MOS quality estimates of speech? Yes, and not only that – its heads used for semi-supervised training can also convey relevant information related to the task.
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Continue readingICASSP article: An empirical study of Conv-TasNet
I’m happy to share the highlights of my first paper with Dolby! We will be presenting this work at ICASSP 2020, in Barcelona.
Several improvements have been proposed to Conv-TasNet – that mostly focus on the separator, leaving its encoder/decoder as a (shallow) linear operator. We propose a (deep) non-linear variant of it, that is based on a deep stack of small filters. With this change, we can improve 0.6-0.9 dB SI-SNRi.
musicnn: an open source, deep learning-based music tagger
The musicnn library (pronounced as “musician”) employs deep convolutional neural networks to automatically tag songs, and the models that are included achieve the best scores in public evaluation benchmarks. These state-of-the-art models have been released as an open-source library that can be easily installed and used. For example, you can use musicnn to tag this emblematic song from Muddy Waters — and it will predominantly tag it as blues!
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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 S
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.