Audio Classification with TensorFlow

Offered By
Coursera Project Network
In this Free Guided Project, you will:

Audio classification with TensorFlow

Creating spectrograms from raw audio data

Showcase this hands-on experience in an interview

Clock2 hours
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this guided project, we are going to create a deep learning model and train it to learn to classify audio files. Audio classification usually does not get the same kind of attention as image classification with deep learning - this could be because audio processing that is typically used in such scenarios is not as straight forward as image data. In this project, we will look at one such processing to convert raw audio into spectrograms before using them in a convolutional neural network. You will need prior programming experience in Python. Some experience with TensorFlow is recommended. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, convolutional neural networks, and optimization algorithms like gradient descent but want to understand how to use TensorFlow to classify audio. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Requirements

Prior programming experience in Python. Conceptual understanding of Neural Networks. Prior experience with TensorFlow and Keras is recommended.

Skills you will develop

Deep LearningArtificial Neural NetworkAudio processingMachine LearningTensorflow

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Introduction

  2. Setup

  3. Explore the Data

  4. Spectrogram

  5. Prepare the Data

  6. Create the Model

  7. Model Training

  8. Predictions

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

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