Deep India WAV

Learning capsules provide developers with tutorials to learn Generative AI in easy-to-consume, bite-size modules. With learning capsules, developers can now get hands-on with Generative AI techniques in addition to ML concepts. Developers looking to dive deep will be provided with sample Amazon SageMaker notebooks for step-by-step code walk through in each learning capsule. Log on to the AWS DeepComposer console and tune in to learning.

Deep India WAV

Explore the portfolio of educational devices designed for developers of all skill levels to learn ML in fun, practical ways. Get started with reinforcement learning with AWS DeepRacer and learn how to build deep learning-based computer vision apps with AWS DeepLens.

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Lyrebird was founded by Alexandre de Brébisson, Kundan Kumar, and Jose Sotelo in 2017 while PhD students at MILA, studying under Yoshua Bengio, who won the Turing Prize in 2019 for his pioneering research into deep learning and neural networks.

The aim of speech denoising is to remove noise from speech signals while enhancing the quality and intelligibility of speech. This example showcases the removal of washing machine noise from speech signals using deep learning networks. The example compares two types of networks applied to the same task: fully connected, and convolutional.

This example uses a subset of the Mozilla Common Voice dataset [1] to train and test the deep learning networks. The data set contains 48 kHz recordings of subjects speaking short sentences. Download the data set and unzip the downloaded file.

The basic deep learning training scheme is shown below. Note that, since speech generally falls below 4 kHz, you first downsample the clean and noisy audio signals to 8 kHz to reduce the computational load of the network. The predictor and target network signals are the magnitude spectra of the noisy and clean audio signals, respectively. The network's output is the magnitude spectrum of the denoised signal. The regression network uses the predictor input to minimize the mean square error between its output and the input target. The denoised audio is converted back to the time domain using the output magnitude spectrum and the phase of the noisy signal [2].

The pre-trained models in Spleeter are U-Nets, i.e., encoder/decoder convolutional neural networks(CNN) with skip connections. The U-Nets are 12 layers deep, 6 layers for the encoder and 6 for the decoder. The models were trained on internal dataset from Deezer using L1-norm loss between masked input mix spectrograms and target spectrograms.

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