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Advanced Convolutional Neural Networks
The final accuracy is 88.21%, showing that it is possible to successfully use CNNs for
textual processing:
Figure 29: Use CNN for text processing
Note that many other non-image applications can also be converted to an image
and classified using a CNN (see, for instance, https://becominghuman.ai/soundclassification-using-images-68d4770df426).
Audio and music
We have used CNNs for images, videos, and text. Now let's have a look to how
variants of CNNs can be used for audio.
So, you might wonder why learning to synthesize audio is so difficult. Well, each
digital sound we hear is based on 16,000 samples per second (sometimes 48,000 or
more) and building a predictive model where we learn to reproduce a sample based
on all the previous ones is a very difficult challenge.
Dilated ConvNets, WaveNet, and NSynth
WaveNet is a deep generative model for producing raw audio waveforms. This
breakthrough technology has been introduced (WaveNet is available at https://
deepmind.com/blog/wavenet-generative-model-raw-audio/) by Google
DeepMind for teaching computers how to speak. The results are truly impressive
and online you find can examples of synthetic voices where the computer learns
how to talk with the voice of celebrities such as Matt Damon. There are experiments
showing that WaveNet improved the current state-of-the-art Text-to-Speech (TTS)
systems, reducing the difference with respect to human voices by 50% for both US
English and Mandarin Chinese. The metric used for comparison is called Mean
Opinion Score (MOS), a subjective paired comparison test. In the MOS tests, after
listening to each sound stimulus, the subjects were asked to rate the naturalness of
the stimulus on a five-point scale from "Bad" (1) to "Excellent" (5).
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