How to apply machine learning and deep learning methods to audio analysis

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To view the code, training visualizations, and more information about the python example at the end of this post, visit the Comet project page. While much of the writing and literature on deep learning concerns computer vision and natural language processing (NLP), audio analysis -- a field that includes automatic speech recognition (ASR), digital signal processing, and music classification, tagging, and generation -- is a growing subdomain of deep learning applications. Some of the most popular and widespread machine learning systems, virtual assistants Alexa, Siri and Google Home, are largely products built atop models that can extract information from audio signals. Many of our users at Comet are working on audio related machine learning tasks such as audio classification, speech recognition and speech synthesis, so we built them tools to analyze, explore and understand audio data using Comet's meta machine-learning platform. This post is focused on showing how data scientists and AI practitioners can use Comet to apply machine learning and deep learning methods in the domain of audio analysis.


How to apply machine learning and deep learning methods to audio analysis

#artificialintelligence

To view the code, training visualizations, and more information about the python example at the end of this post, visit the Comet project page. While much of the writing and literature on deep learning concerns computer vision and natural language processing (NLP), audio analysis -- a field that includes automatic speech recognition (ASR), digital signal processing, and music classification, tagging, and generation -- is a growing subdomain of deep learning applications. Some of the most popular and widespread machine learning systems, virtual assistants Alexa, Siri and Google Home, are largely products built atop models that can extract information from audio signals. Many of our users at Comet are working on audio related machine learning tasks such as audio classification, speech recognition and speech synthesis, so we built them tools to analyze, explore and understand audio data using Comet's meta machine-learning platform. This post is focused on showing how data scientists and AI practitioners can use Comet to apply machine learning and deep learning methods in the domain of audio analysis.


Ok Google: How to do Speech Recognition?

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Speech recognition is the task of detecting spoken words. There are many techniques to do Speech Recognition. In this post, we will go through some background required for Speech Recognition and use a basic technique to build a speech recognition model. The code is available on GitHub. For the techniques mentioned in this post, check this Jupyter Notebook. Let's take a step back and understand what audio actually is. We all listen to music on our computers/phones.


Speech Classification Using Neural Networks: The Basics

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Recently I started working on a speech classification problem, as I know very little about speech/audio processing, I had to recap the very basics. In this post, I want to go over some of the things I learned. For this purpose, I want to work on the "speech MNIST" dataset, i.e, a set of recorded spoken digits. You can find the dataset here. As mentioned in the title, this is a classification problem, we get a recording and we need to predict the spoken digit in it.


Machine Learning with Signal Processing Techniques

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Stochastic Signal Analysis is a field of science concerned with the processing, modification and analysis of (stochastic) signals. Anyone with a background in Physics or Engineering knows to some degree about signal analysis techniques, what these technique are and how they can be used to analyze, model and classify signals. Data Scientists coming from a different fields, like Computer Science or Statistics, might not be aware of the analytical power these techniques bring with them. In this blog post, we will have a look at how we can use Stochastic Signal Analysis techniques, in combination with traditional Machine Learning Classifiers for accurate classification and modelling of time-series and signals. At the end of the blog-post you should be able understand the various signal-processing techniques which can be used to retrieve features from signals and be able to classify ECG signals (and even identify a person by their ECG signal), predict seizures from EEG signals, classify and identify targets in radar signals, identify patients with neuropathy or myopathyetc from EMG signals by using the FFT, etc etc. In this blog-post we'll discuss the following topics: You might often have come across the words time-series and signals describing datasets and it might not be clear what the exact difference between them is.