February 2020
Intermediate to advanced
328 pages
8h 19m
English
Content preview from Deep Learning with R Cookbook
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Getting ready
In this recipe, we will train a neural network that learns to classify sound waves based on the frequency spectrum. We will work with the Google speech commands dataset for this. It was created by the TensorFlow and AIY teams to showcase a speech recognition example using the TensorFlow API. It contains recordings of many spoken words, and each recording is sampled at 16 kHz. It can be downloaded from https://storage.cloud.google.com/download.tensorflow.org/data/speech_commands_v0.01.tar.gz. We will use the tuneR package to read the WAV files and the seewave package to perform STFT on the audio signal.
Let's start by importing the required libraries:
library(seewave)library(stringr)library(keras)library(tuneR)library(splitstackshape) ...
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