平成 29 年度電気関係学会四国支部連合大会 講演論文集 (2017 愛媛大学)
2017 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS (EHIME)

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Deep Learning Imitating Relationshipbetween Ears and Brain for Temporal Data
Shinsaburo KITTAKA
Yoko UWATE( Tokushima University)

Yoshifumi NISHIO

1. Introduction

3. Simulation results

Artificial intelligence (AI) is hot topic among not only researchers but also the public. Because AI is hoped as newlabor force. Japanese cabinet publicly disclosed the prediction that japanese population is decreasing and drops
to below 100 million by the year 2050. From this prediction, we expect labor force to become decreasing. However,computer capability is improving on Moore’s Law without
population and labor force decreasing. So AI will serveas labor force in foreseeable future. And now, most of AI
discriminates object from images. Because current AI is focused on image processing, and the structure is similar torelationship between eyes and brain.
In this study, we set up the structure of deep learningsimilar to relationship between ears and brain. And it discriminates not image that has instantaneous data but musicthat has temporal data. We hope AI to expand the rangeof labor force.

In simulation, network discriminates music scales. We
prepare data set for learning and test from youtube (URL:
https://www.youtube.com/watch?v=IVdp0uBdrMM). We
convert MP3 files from youtube to WAV files with 44,100
Hz sampling frequency. The number of data is 1,360,291.
And we divide data set into each 5000 data.
The amplitude of waveform means just sound volume. It
is not necessary to discriminate music scales. So we normalize the maximum value of data to be 1 in each data.
Finally, we change all data to positive. Because the activation function in network outputs 0 if input is negative.
Figure. 2 shows the activation function in network.

2. Proposed method
Deep learning consists of input layers, convolutional layers, pooling layers, fully connected layers and output layers.
Convolutional layers make many layers from input layers
and augment feature quantity. Pooling layers have filter
and pick up maximal value. Fully connected layers connect
every neuron in one layer to every neuron in another layer.
We focus on pooling layers. Usual pooling layers pick up
and transmit maximum value. We change the value to thediﬀerence of maximum value and minimum value as Fig. 1.

Figure 2: Rectified Linear Unit.
The number of input data is 5000 data in a single learning, and we input data in line. We define the method that is
picking up maximum value in pooling layer as conventional
method.

Table 1: The discriminant accuracy
conventional proposedaccuracy[%]
82.1
83.4

From Table 1, the proposed method is only a little bitbetter than the conventional method.

4. Conclusions
We change pooling system in pooling layer in deep learning for temporal data. Conventional pooling layers pick up
and transmit maximum value. We change the transmitting
value to the diﬀerence of maximum value and minimum
value as proposed method. Because the ear converts fluctuation to electrical signal based on displacement of hair.
In simulation result, the proposed method obtains a little bit better accuracy than the conventional method. Weconsider that the diﬀerence of maximum value and minimum value has more information than maximum value fortemporal data.
In future work, we make networking component more
and more like the relationship between ear and brain for
discriminating temporal data.

Figure 1: Changing system in pooling layers.
The ear uses hair sells to convert fluctuation to electricalsignals. The firing of hair sell is based on displacement ofhair. We consider that it is the most diﬀerence between eyes
and ears. So we change pooling layers as proposed method.

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