- Neural Networks with Keras Cookbook
- V Kishore Ayyadevara
- 169字
- 2021-07-02 12:46:33
Classifying common audio
In the previous sections, we have understood the strategy to perform modeling on a structured dataset and also on unstructured text data.
In this section, we will be learning about performing a classification exercise where the input is raw audio.
The strategy we will be adopting is that we will be extracting features from the input audio, where each audio signal is represented as a vector of a fixed number of features.
There are multiple ways of extracting features from an audio—however, for this exercise, we will be extracting the Mel Frequency Cepstral Coefficients (MFCC) corresponding to the audio file.
Once we extract the features, we shall perform the classification exercise in a way that is very similar to how we built a model for MNIST dataset classification—where we had hidden layers connecting the input and output layers.
In the following section, we will be performing classification on top of an audio dataset where there are ten possible classes of output.
- Learning Scala Programming
- C#編程入門指南(上下冊)
- Java持續交付
- Securing WebLogic Server 12c
- Oracle JDeveloper 11gR2 Cookbook
- JAVA程序設計實驗教程
- 移動界面(Web/App)Photoshop UI設計十全大補
- Express Web Application Development
- Raspberry Pi Home Automation with Arduino(Second Edition)
- JavaScript機器人編程指南
- jQuery技術內幕:深入解析jQuery架構設計與實現原理
- Vue.js光速入門及企業項目開發實戰
- Ext JS 4 Plugin and Extension Development
- Secret Recipes of the Python Ninja
- Python Projects for Kids