Edit sound with Python NumPy: Improve code performance 1000x

Edit sound with Python NumPy: Improve code performance 1000x, available at $74.99, has an average rating of 4.8, with 59 lectures, based on 18 reviews, and has 665 subscribers.

You will learn about Code optimization in Python using the NumPy library Sound processing in Python using the MoviePy library Fundamentals of digital images Applying code optimization to binarize digital images This course is ideal for individuals who are Engineering students or Engineering professionals or Data Scientists or Engineering & Programming hobbyists or Programmers It is particularly useful for Engineering students or Engineering professionals or Data Scientists or Engineering & Programming hobbyists or Programmers.

Enroll now: Edit sound with Python NumPy: Improve code performance 1000x

Summary

Title: Edit sound with Python NumPy: Improve code performance 1000x

Price: $74.99

Average Rating: 4.8

Number of Lectures: 59

Number of Published Lectures: 59

Number of Curriculum Items: 63

Number of Published Curriculum Objects: 63

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Code optimization in Python using the NumPy library
Sound processing in Python using the MoviePy library
Fundamentals of digital images
Applying code optimization to binarize digital images

Who Should Attend

Engineering students
Engineering professionals
Data Scientists
Engineering & Programming hobbyists
Programmers

Target Audiences

Engineering students
Engineering professionals
Data Scientists
Engineering & Programming hobbyists
Programmers

Programming is one of the most flexible fields I know of. You can create a program that achieves a certain task in so many ways. However, that does not mean that all ways are equal. Some are better than others.

That is especially visible when your program has to work with big data. Working with big data means working with gigantic arrays and matrices.

You can create a program that achieves the same task like the other one, but it does so 1000 times faster. It all depends on how you code and which coding practices you use.

And this is what you will learn here. You will learn the good and the bad coding practices, so that you would learn to code the right way when dealing with big data.

In this 100% project based course, we will use Python, the Numpy and the Moviepy library to create a fully functional sound processing program.

This program will import your videos in sequence, extract their audio, automatically identify the silent intervals in that audio, and then cut them out while still keeping some silence on the edges to preserve a bit of pause in between sentences.

Sound processing naturally deals with millions and millions array elements and so it really matters how we write that program. We will do it in a bad way and in a good way, because I want you to see both sides of the coin.

In the end, you will see that the last version of your Python Numpy code will be more than 1000 times faster than the first version, and so, you will see how to code and how definitely not to code.

Finally, I really want you to see that this knowledge is universal and can be applied in other fields as well, not only audio processing. And therefore, in the last section, there will be an assignment in computer vision.

Digital images are in fact, gigantic matrices, and so, it really matters how you handle them in the code. We will build a small program that can binarize these images and we will also do it in a good and in a bad way.

We will use the Python image processing library called Pillow to process all this big data inside the image matrices.

After this course, you will know how to approach programming in the right way from the beginning. Take a look at some of my free preview videos and if you like what you see, then, ENROLL NOW and let’s get started! I’ll see you inside.

Course Curriculum

Chapter 1: Intro to course structure and Python environment installation

Lecture 1: Welcome & Course Structure

Lecture 2: Intro to (Linux & macOS Terminal) & (Windows Command Prompt)

Lecture 3: Python installation instructions

Lecture 4: Python installation – Windows 10

Lecture 5: Python installation – Ubuntu

Lecture 6: Python installation – macOS

Chapter 2: Version 1: Building the silence removal program (very bad version)

Lecture 1: Importing the necessary Python libraries

Lecture 2: Importing videos & extracting audio with MoviePy

Lecture 3: Plotting audio using Matplotlib

Lecture 4: Moving sound from left to right ear and vice versa

Lecture 5: Creating the array for storing nonsilent audio samples

Lecture 6: Using MoviePy functions to cut & merge videos

Lecture 7: Rules for determining a silent interval

Lecture 8: Converting audio samples to seconds

Lecture 9: Determining audio samples per second & performing the 1st video cut

Lecture 10: Cutting out silence in the video

Lecture 11: Performing the last video cut & exploring possible exceptions to the program

Lecture 12: Dealing with exceptions in video processing 1

Lecture 13: Dealing with exceptions in video processing 2

Lecture 14: Python sound processing code – Summary

Lecture 15: Plotting the new & exporting the new video + measuring performance time

Lecture 16: The results of the sound processing program

Lecture 17: Test files & Python code for this section

Chapter 3: Version 2: Code restructuring & improving (bad version)

Lecture 1: Restructuring the program

Lecture 2: Expanding the capabilities of the program 1

Lecture 3: Expanding the capabilities of the program 2

Lecture 4: Expanding the capabilities of the program 3

Lecture 5: Expanding the capabilities of the program 4

Lecture 6: Expanding the capabilities of the program 5

Lecture 7: Expanding the capabilities of the program 6

Lecture 8: Code Optimization 1: Identifying a very bad coding practice

Lecture 9: Code Optimization 2: Exploring the alternative approach

Lecture 10: Code Optimization 3: Implementing the alternative approach in the code

Lecture 11: Code Optimization 4: Comparing the performance of the two coding practices

Lecture 12: Test files & Python code for this section

Chapter 4: Version 3: Code optimization: giant leap using Numpy functions (good version)

Lecture 1: Intro to vectorization

Lecture 2: Exploring the Numpy "where" function

Lecture 3: The boolean AND vs OR logic

Lecture 4: Clarification on the boolean logic in Python 3

Lecture 5: Comparing the end result of all the 3 versions in the Python code

Lecture 6: Comparing the performance time of all the versions in the Python code

Lecture 7: Test files & Python code for this section

Chapter 5: Version 4: Taking advantage of NumPy functions 100% (excellent version)

Lecture 1: Revision of the previous section

Lecture 2: Numpy array difference calculation: Sequential VS Vectorization method

Lecture 3: Silence interval condition checking using the Numpy where function

Lecture 4: Applying cutting & merging operations using the newest method 1

Lecture 5: Applying cutting & merging operations using the newest method 2

Lecture 6: Applying cutting & merging operations using the newest method 3

Lecture 7: General recap of Version 4

Lecture 8: Adding extra features to Version 4

Lecture 9: Final improvement in concatenating video clips

Lecture 10: Test files & Python code for this section

Chapter 6: Assignment section – Binary image creation with NumPy (Computer Vision)

Lecture 1: Intro to digital images in computer vision (grayscale & color)

Lecture 2: Intro to binary images in computer vision

Lecture 3: Building the image processing program for creating binarized images

Lecture 4: Instructions for the assignment in computer vision

Lecture 5: Test files & Python code for this section

Chapter 7: Last Words

Lecture 1: Thank You!

Lecture 2: Well done! You did it! But don't stop here! Keep going forward!

Instructors

Mark Misin Engineering Ltd
Math, Control Systems, Python, Mechanics: Statics & Dynamics

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5 stars: 14 votes

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