[PYTHON] I tried to classify guitar chords in real time using machine learning

Purpose

I was playing the guitar as a hobby, and I was wondering if AI could correctly identify the guitar sound. This time, I will try to classify 9 types of code in real time using a normal Neural Network and a simple CNN.

environment

Use Python. Install the following libraries. ・ Pyaudio ・ Chainer ・ Keras ・ Sklearn ・ Pandas

agenda

1. Acquisition of sound wave shape, Fourier transform
2. Neural Network
3. CNN
4. Future issues

1. Acquisition of sound wave shape, Fourier transform

A program that acquires voice waveforms in real time, Fourier transforms them, normalizes them, and writes the results to a csv file. I'm sorry, but I'm not very familiar with Fourier transform because I'm not a specialist. It is better to study and use the program because I referred to other articles, but it seems that it can be easily done with numpy. After recording the waveforms of all the codes in a csv file, label them and combine them into one file. This time, we classify 10 types of spectra, C, D, G, A, Am, F, Fm, B, Bm, and silence.

data-kakikomi.py

import pyaudio
import numpy as np
import matplotlib.pyplot as plt
import math
import csv
CHUNK = 1024
RATE = 44100 #Sampling frequency
P = pyaudio.PyAudio()

stream = P.open(format=pyaudio.paInt16, channels=1, rate=RATE, frames_per_buffer=CHUNK, input=True, output=False)
x = np.arange(1,1025,1)
freq = np.linspace(0, RATE, CHUNK)

#Normalization
def min_max(x, axis=None):
    min = x.min(axis=axis, keepdims=True)
    max = x.max(axis=axis, keepdims=True)
    result = (x-min)/(max-min)
    return result

o = open('fmcode.csv','a') #Change the file for each code
writer = csv.writer(o, lineterminator=',\n')
while stream.is_active():
    try:
        input = stream.read(CHUNK, exception_on_overflow=False)
        #Convert from buffer to ndarray
        ndarray = np.frombuffer(input, dtype='int16')

        #Fourier transform
        f = np.fft.fft(ndarray)
        
        #frequency
        freq = np.fft.fftfreq(CHUNK, d=44100/CHUNK)
        Amp = np.abs(f/(CHUNK/2))**2
        Amp = min_max(Amp)
        writer.writerow(Amp)
        print(Amp)

        #Display spectrum after Fourier transform
        line, = plt.plot(freq[1:int(CHUNK/2)], Amp[1:int(CHUNK/2)], color='blue')
        plt.pause(0.01)
        plt.ylim(0,1)
        ax = plt.gca()
        ax.set_xscale('log')
        line.remove()
    except KeyboardInterrupt:
        break

stream.stop_stream()
stream.close()
P.terminate()
f.close()

print('Stop Streaming')

The waveform after Fourier transform is displayed as shown in the figure below.

2. Neural Network Next, create a learning model using Chainer (NN)

chainer_NN.py

import chainer
from chainer import Chain, optimizers, iterators, training, datasets, Variable
from chainer.training import extensions
import chainer.functions as F
import chainer.links as L
import numpy as np
import pandas as pd
from chainer import serializers
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

#Neural network class by chainer
class NN(Chain):
    def __init__(self, in_size, hidden_size, out_size):
        super(NN, self).__init__(
            xh = L.Linear(in_size, hidden_size),
            hh = L.Linear(hidden_size, hidden_size),
            hy = L.Linear(hidden_size, out_size)
        )
    
    def __call__(self, x):
        h1 = F.sigmoid(self.xh(x))
        #h1 = F.dropout(F.relu(self.xh(x)), train=train)
        h2 = F.sigmoid(self.hh(h1))
        y = F.softmax(self.hy(h2))
        return y

#Data read
data1 = pd.read_csv("data.csv")
X = data1.iloc[:, 0:1024] #140 #no_outline:106
Y = data1.iloc[:, 1025] #↑+2
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.1, random_state = 0)

X_train = X_train.values
Y_train = Y_train.values

X_test = X_test.values
Y_test = Y_test.values


#The part required when reading data with chainer
X_train = np.array(X_train.astype(np.float32))
Y_train = np.ndarray.flatten(np.array(Y_train.astype(np.int32)))

X_test = np.array(X_test.astype(np.float32))
Y_test = np.ndarray.flatten(np.array(Y_test.astype(np.int32)))

#Number of units and epochs in each layer
n_in_units = 1024
n_out_units = 10
n_hidden_units = 100
n_epoch = 3000


#Reflect the determined number of units in the neural network,
model = L.Classifier(NN(in_size = n_in_units, hidden_size = n_hidden_units, out_size = n_out_units))
optimizer = optimizers.Adam()
optimizer.setup(model)

#Training part
print("Train")
train, test = datasets.split_dataset_random(datasets.TupleDataset(X_train, Y_train), int(len(Y_train)*0.9))
train_iter = iterators.SerialIterator(train, int(len(Y_train)*0.9))
test_iter = iterators.SerialIterator(test, int(len(Y_train)*0.1), False, False)
updater = training.StandardUpdater(train_iter, optimizer, device=-1)
trainer = training.Trainer(updater, (n_epoch, "epoch"), out="result")
trainer.extend(extensions.Evaluator(test_iter, model, device=-1))
trainer.extend(extensions.LogReport(trigger=(10, "epoch"))) #Log output every 10 epochs
trainer.extend(extensions.PrintReport( ["epoch", "main/loss", "validation/main/loss", "main/accuracy", "validation/main/accuracy"])) 
#Epoch, learning loss, test loss, learning correct answer rate, test correct answer rate, elapsed time
trainer.extend(extensions.ProgressBar()) #Progress bar output
trainer.run()

#Save the trained model created in the training part
serializers.save_npz("model.npz", model)


#Test part, result output
C_list1 = []
print("Test")
print("y\tpredict")
for i in range(len(X_test)):
    x = Variable(X_test[i])
    y_ = np.argmax(model.predictor(x=x.reshape(1,len(x))).data, axis=1)
    y = Y_test[i]
    print(y+2, "\t", y_+2)
    C = y_ - y
    C_list1 = np.append(C_list1,C)
A = np.count_nonzero(C_list1 == 0)
p = A / (len(C_list1))
print(p)

The learned results are shown below.

0.6749311294765841

Normal NN is not very accurate.

3. CNN Next, create a learning model using keras (CNN)

CNN.py

import numpy as np
#Data loading and preprocessing
from keras.utils import np_utils
#Build CNN with keras
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
import pandas as pd
import time

f_model = './model'

#Time measurement
import time
correct = 10
data = pd.read_csv("data.csv")
X = data.iloc[:, 0:1024]
Y = data.iloc[:, 1025]

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.1, random_state = 0)
X_train = X_train.to_numpy()
X_train = X_train.reshape(3264,32,32,1)
X_train = X_train.astype('float32')

Y_train = Y_train.to_numpy()
Y_train = np_utils.to_categorical(Y_train, correct)

X_test = X_test.to_numpy()
X_test = X_test.reshape(363,32,32,1)
X_test = X_test.astype('float32')

Y_test = Y_test.to_numpy() 
Y_test = np_utils.to_categorical(Y_test, correct)

model = Sequential()

model.add(Conv2D(filters=10, kernel_size=(3,3),padding='same', input_shape=(32,32,1), activation='relu'))
model.add(Conv2D(32,1,activation='relu'))
model.add(Conv2D(64,1,activation='relu'))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])

startTime = time.time()

history = model.fit(X_train, Y_train, epochs=200, batch_size=100, verbose=1, validation_data=(X_test, Y_test))
 
score = model.evaluate(X_test, Y_test, verbose=0)

print('Test Loss:{0:.3f}'.format(score[0]))
print('Test accuracy:{0:.3}'.format(score[1]))
#processing time
print("time:{0:.3f}sec".format(time.time() - startTime))

json_string = model.to_json()

model.save('model_CNN.h5')

The learning results are shown below

Test Loss:0.389
Test accuracy:0.948
time:327.122sec

The correct answer rate was 94.8%, which was much better.

Finally, try to classify the code in real time with CNN

code_detector.py

from keras.models import load_model
import pyaudio
import numpy as np
import matplotlib.pyplot as plt
import math

CHUNK = 1024
RATE = 44100 #Sampling frequency
P = pyaudio.PyAudio()

stream = P.open(format=pyaudio.paInt16, channels=1, rate=RATE, frames_per_buffer=CHUNK, input=True, output=False)

def min_max(x, axis=None):
    min = x.min(axis=axis, keepdims=True)
    max = x.max(axis=axis, keepdims=True)
    result = (x-min)/(max-min)
    return result

model = load_model('model_CNN.h5')

def detect(pred):
    a = ["C","D","G","Bm","B","","A","Am","F","Fm"]
    pred_label = a[np.argmax(pred[0])]
    score = np.max(pred)
    if pred_label != "":
        print(pred_label,score)

while stream.is_active():
    try:
        input = stream.read(CHUNK, exception_on_overflow=False)
        #Convert from buffer to ndarray
        ndarray = np.frombuffer(input, dtype='int16')
        line, = plt.plot(ndarray, color='blue')
        plt.pause(0.01)
        f = np.fft.fft(ndarray)
        Amp = np.abs(f/(CHUNK/2))**2
        Amp = min_max(Amp)
        Amp = Amp.reshape(1,32,32,1)
        Amp = Amp.astype('float32')
        pred = model.predict(Amp)
        
        detect(pred)
        plt.ylim(-200,200)
        line.remove()
    except KeyboardInterrupt:
        break

stream.stop_stream()
stream.close()
P.terminate()
print('Stop Streaming')

When you play the C chord

C 1.0
C 1.0
C 1.0
C 1.0
C 1.0
C 1.0
C 1.0
C 1.0
C 1.0
C 0.99999833
C 1.0
C 0.9999988
C 1.0
C 1.0
C 1.0
G 0.98923177

When you play the D chord

D 0.9921374
D 1.0
D 1.0
D 1.0
D 1.0
D 1.0
D 0.99915206
Bm 0.9782265
D 1.0
D 0.967693
Bm 0.43872046
D 0.5228199
D 0.9998678
D 0.99264586

When playing the Am chord

A 0.7428425
Am 0.98781455
Am 1.0
Am 1.0
Am 1.0
Am 1.0
Am 0.99081403
Am 0.9998661
Am 0.98926556
Am 0.9721039
Am 0.9999999
Am 0.99899584
A 0.7681879
Am 0.59727216
Am 0.77573067

When you play the F chord

Fm 0.54534096
F 1.0
F 0.4746885
F 0.99983275
F 0.9708171
F 1.0
F 0.9999441
F 0.99999964
C 0.50546944
F 0.9999746
F 1.0
F 1.0
F 0.9999999
F 0.966004
C 0.79529727
F 1.0
F 0.99999976

When playing the Fm chord

Fm 0.9999492
Fm 1.0
Fm 1.0
Fm 0.99058926
Fm 1.0
Fm 0.99991775
Fm 0.9677996
F 0.96835506
Fm 1.0
Fm 0.9965939
Am 0.63923794
C 0.8398564
Fm 0.91774964
Am 0.9995415

Future tasks

Personally, I was surprised that F and Fm could be distinguished. Currently, the audio data is acquired only by the main guitar, so the accuracy will drop depending on other guitars and players. Is it a future task to create a model by increasing the number of data?