令和元年度電気関係学会四国⽀部連合⼤会 講演論⽂集 (2019 新居浜⾼専)
2019 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS (NIIHAMA)

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Voice Classification Using Surrogate Datawith 1D-Convolutional Neural Networks
Tomiyuki FURUGAKI Tomoya TAKATA
Yoko UWATE Yoshifumi NISHIO(Tokushima University)
1. Introduction

3. Simulation Result

Convolutional Neural Network (CNN) was devised basedon neurophysiological on the visual cortex of the brain of
the creature. It has attracted attention mainly for use inimage recognition. However, in recent years, CNN has alsobeen use to audio signal processing. In this research, weused 1D-CNN as a classification model, we focus on whichpart of time series data is eﬀective for using 1D-CNN byusing surrogate data.

Figure 1 shows the structure of CNN which we use in thisstudy. It should be noted that the dimension of the input
layer is one dimension.
Forty pieces of data for 6 seconds each are prepared. After sampling them, surrogate is created. Furthermore, eachdata is divided into three pieces. The number of the training time series data is 90. The number of the test time series
data is 30. Each data is classified with 1D-CNN. Table 1
shows the results of our research. We investigate the average of ten times of test accuracy. Test accuracies of RSSD
and AAFTSD are lower than test accuracy of the originaltime series data. Test accuracy when using FTSD is similar
to that when using the original time series data.

2. Proposed Method
In this research, we classify three types of voices. Eachtime series data is sampled at a sampling frequency 3000.
These data are classified using CNN. Three types of surrogate data are created. The following explanations (a),(b) and (c) describe how to create three types of surrogatedata.
(a) Random Shuﬄe Surrogate Data (RSSD)
x(n) means time function. n means time. It is RSSD
data that changes the order of n at random.
(b) Fourier Transform Surrogates Data (FTSD)

X(ω) =n∑2πknx(n)e−i N(1)n=1
nx(n) =2πkn
1 ∑X(ω)ei NN(2)Figure 1: Structure of CNN.

n=1

Equations (1) and (2) show discrete Fourier Transform(DFT) and Inverse Discrete Fourier Transform (IDFT). kmeans frequency. N (= 15000) means the number of thesamples.

Table 1: Test accuracy
original data
RSSD
FTSD
AAFTSD

Step 1. Calculate DFT X(ω) of x(n).
Step 2. Randomize the phase of X(ω).
Step 3. Calculate IDFT randomized X(ω).

test accuracy(%)
70.9
58.2
71.1
60.2

4. Conclusion

FTSD is made in this way.

In this study, we carried out 3-value classification withsurrogate data. Then, we understood that test accuracies
of RSSD and AAFTSD were lower than test accuracy whenusing the original time series data. RSSD and AAFTSD did
not store power spectrum. Therefore, it was understandable
that 1D-CNN recognized power spectrum.
However, we do not know how much the accuracy is reduced by the similarity to the power spectrum of originaldata. In the future, we will choose more similar power spectrum of surrogate data than that of AAFTSD.

(c) Amplitude Adjusted Fourier Transform Surrogates Data(AAFTSD)
Step 1. Prepare random numbers R(n) according to thestandard normal distribution.
Step 2. Sorting R(n) in the same size relation as x(n).
Step 3. Create R′ (n) which is FTSD of sorted R(n).
Step 4. Sorting x(n) in the same size relation as R′ (n).
AAFTSD is made in this way.

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