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tutorial_processing.html
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<!doctype html>
<html lang="en">
<head>
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<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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<title>SpeechBrain: Speech Processing</title>
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<li class="nav-item active"><a class="nav-link" href="index.html">Home</a></li>
<li class="nav-item"><a class="nav-link" href="about.html">About SpeechBrain</a>
<li class="nav-item"><a class="nav-link" href="contributing.html">Contributing</a>
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<h2>SpeechBrain Tutorials</h2>
<h3>Speech Processing</h3>
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<!--================End Home Banner Area =================-->
<div class="alert alert-primary" role="alert" style="text-align: center; font-size: 24px;">
🎉 SpeechBrain 1.0 is out. <a href="https://colab.research.google.com/drive/1IEPfKRuvJRSjoxu22GZhb3czfVHsAy0s?usp=sharing"><strong> Check out what's new!</strong></a>
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<article class="row blog_item">
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<div class="blog_info text-right">
<div class="post_tag">
<a class="active" href="#">Speech Processing</a>
</div>
<ul class="blog_meta list">
<li><a href="about.html">Ravanelli M.<i class="lnr lnr-user"></i></a></li>
<li><a href="#">Jan. 2021<i class="lnr lnr-calendar-full"></i></a></li>
<li><a href="#">Difficulty: easy<i class="lnr lnr-cog"></i></a></li>
<li><a href="#">Time: 20min<i class="lnr lnr-hourglass"></i></a></li>
</ul>
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<div class="blog_details">
<h2>Speech Augmentation</h2>
<p>A popular saying in machine learning is "there is no better data than more data". However, collecting new data can be expensive
and we must cleverly use the available dataset. One popular technique is called speech augmentation. The idea is to artificially
corrupt the original speech signals to give the network the "illusion" that we are processing a new signal. This acts as a powerful regularizer,
that normally helps neural networks improving generalization and thus achieve better performance on test data.</p>
<a href="https://colab.research.google.com/drive/1JJc4tBhHNXRSDM2xbQ3Z0jdDQUw4S5lr?usp=sharing" class="blog_btn">Open in Google Colab</a>
</div>
</div>
</div>
</article>
<article class="row blog_item">
<div class="col-md-3">
<div class="blog_info text-right">
<div class="post_tag">
<a class="active" href="#">Speech Processing</a>
</div>
<ul class="blog_meta list">
<li><a href="about.html">Ravanelli M.<i class="lnr lnr-user"></i></a></li>
<li><a href="#">Jan. 2021<i class="lnr lnr-calendar-full"></i></a></li>
<li><a href="#">Difficulty: easy<i class="lnr lnr-cog"></i></a></li>
<li><a href="#">Time: 20min<i class="lnr lnr-hourglass"></i></a></li>
</ul>
</div>
</div>
<div class="col-md-9">
<div class="blog_post">
<div class="blog_details">
<h2>Fourier Transform and Spectrograms</h2>
<p>In speech and audio processing, the signal in the time-domain is often transformed into another domain.
Ok, but why do we need to transform an audio signal? Some speech characteristics/patterns of the signal (e.g, pitch, formats)
might not be very evident when looking at the audio in the time-domain. With properly designed transformations,
it might be easier to extract the needed information from the signal itself. The most popular transformation is the
Fourier Transform, which turns the time-domain signal into an equivalent representation in the frequency domain.
In the following sections, we will describe the Fourier transforms along with other related transformations such as
Short-Term Fourier Transform (STFT) and spectrograms.</p>
<a href="https://colab.research.google.com/drive/18IgBv3Ip0rWXjYoZywttSmW7Y2AIK1vJ?usp=sharing" class="blog_btn">Open in Google Colab</a>
</div>
</div>
</div>
</article>
<article class="row blog_item">
<div class="col-md-3">
<div class="blog_info text-right">
<div class="post_tag">
<a class="active" href="#">Speech Processing</a>
</div>
<ul class="blog_meta list">
<li><a href="about.html">Ravanelli M.<i class="lnr lnr-user"></i></a></li>
<li><a href="#">Jan. 2021<i class="lnr lnr-calendar-full"></i></a></li>
<li><a href="#">Difficulty: easy<i class="lnr lnr-cog"></i></a></li>
<li><a href="#">Time: 20min<i class="lnr lnr-hourglass"></i></a></li>
</ul>
</div>
</div>
<div class="col-md-9">
<div class="blog_post">
<div class="blog_details">
<h2>Speech Features</h2>
<p>Speech is a very high-dimensional signal. For instance, when the sampling frequency is 16 kHz,
we have 16000 samples for each second. Working with such very high dimensional data can be critical from a machine learning perspective.
The goal of feature extraction is to find more compact ways to represent speech.</p>
<a href="https://colab.research.google.com/drive/1CI72Xyay80mmmagfLaIIeRoDgswWHT_g?usp=sharing" class="blog_btn">Open in Google Colab</a>
</div>
</div>
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</article>
<article class="row blog_item">
<div class="col-md-3">
<div class="blog_info text-right">
<div class="post_tag">
<a class="active" href="#">Speech Processing</a>
</div>
<ul class="blog_meta list">
<li><a href="about.html">Ravanelli M.<i class="lnr lnr-user"></i></a></li>
<li><a href="#">Feb. 2021<i class="lnr lnr-calendar-full"></i></a></li>
<li><a href="#">Difficulty: medium<i class="lnr lnr-cog"></i></a></li>
<li><a href="#">Time: 20min<i class="lnr lnr-hourglass"></i></a></li>
</ul>
</div>
</div>
<div class="col-md-9">
<div class="blog_post">
<div class="blog_details">
<h2>Environmental corruption</h2>
<p>In realistic speech processing applications, the signal recorded by the microphone is corrupted by noise and reverberation.
This is particularly harmful in distant-talking (far-field) scenarios, where the speaker and the reference microphone are distant
(think about popular devices such as Google Home, Amazon Echo, Kinect, and similar devices).</p>
<a href="https://colab.research.google.com/drive/1mAimqZndq0BwQj63VcDTr6_uCMC6i6Un?usp=sharing" class="blog_btn">Open in Google Colab</a>
</div>
</div>
</div>
</article>
<article class="row blog_item">
<div class="col-md-3">
<div class="blog_info text-right">
<div class="post_tag">
<a class="active" href="#">Speech Processing</a>
</div>
<ul class="blog_meta list">
<li><a href="about.html">Grondin F. & Aris W.<i class="lnr lnr-user"></i></a></li>
<li><a href="#">Jan. 2021<i class="lnr lnr-calendar-full"></i></a></li>
<li><a href="#">Difficulty: medium<i class="lnr lnr-cog"></i></a></li>
<li><a href="#">Time: 20min<i class="lnr lnr-hourglass"></i></a></li>
</ul>
</div>
</div>
<div class="col-md-9">
<div class="blog_post">
<div class="blog_details">
<h2>Multi-microphone Beamforming</h2>
<p>Using a microphone array can be very handy to improve the signal quality
(e.g. reduce reverberation and noise) prior to performing speech recognition tasks.
Microphone arrays can also estimate the direction of arrival of a sound source, and this information can later
be used to "listen" in the direction of the source of interest.</p>
<a href="https://colab.research.google.com/drive/1UVoYDUiIrwMpBTghQPbA6rC1mc9IBzi6?usp=sharing" class="blog_btn">Open in Google Colab</a>
</div>
</div>
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<h3>About Us</h3>
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<p style="text-align:justify">SpeechBrain isn't a company or an association.
It is an open-source toolkit and a community created by Dr. Mirco Ravanelli and co-created by Dr. Titouan Parcollet.
We aim at making speech technologies more accessible for the community. </p>
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<p>Thanks to our sponsors, we often recruit talented candidates to continue expanding the functionalities of SpeechBrain.
Feel free to contact us at: speechbrainproject@gmail.com</p>
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