An introduction to audio processing and machine learning using Python

An introduction to audio processing and machine learning using Python

  • October 5, 2019
Table of Contents

An introduction to audio processing and machine learning using Python

The pyAudioProcessing library classifies audio into different categories and genres. At a high level, any machine learning problem can be divided into three types of tasks: data tasks (data collection, data cleaning, and feature formation), training (building machine learning models using data features), and evaluation (assessing the model). Features, defined as ‘individual measurable propert[ies] or characteristic[s] of a phenomenon being observed,’ are very useful because they help a machine understand the data and classify it into categories or predict a value.

Source: opensource.com

Tags :
Share :
comments powered by Disqus

Related Posts

Understanding Convolutional Neural Networks

Understanding Convolutional Neural Networks

A Convolutional Neural Network (CNN) is a class of deep, feed-forward artificial neural networks most commonly applied to analyzing visual imagery. The architecture of these networks was loosely inspired by biological neurons that communicate with each other and generate outputs dependent on the inputs. Though work on CNNs started in the early 1980s, they only became popular with recent technology advancements and computational capabilities that allow the processing of large amounts of data and the training of sophisticated algorithms in a reasonable amount of time.

Read More
The Effects of Mixing Machine Learning and Human Judgment

The Effects of Mixing Machine Learning and Human Judgment

In 1997 IBM’s Deep Blue software beat the World Chess Champion Garry Kasparov in a series of six matches. Since then, other programs have beaten human players in games ranging from Jeopardy to Go. Inspired by his loss, Kasparov decided in 2005 to test the success of Human+AI pairs in an online chess tournament.2

Read More