rezachu / emotion_recognition_cnn

Speech Emotion Recognition with Convolution Neural Network

Author @rezachu

I. Introduction

• This is a CNN Speech Emotion Recognition Model I found on GitHub.
• There is no paper to reference according to the repository author.

II. Package Required

• Please refers to the requirement file: requirements.txt

III. To Run

• Please run the notebook named: CNN_emotion_recognition.ipynb
• Please create a ./data/ folder and put all of the data inside.
• Please create a ./model/ folder and set it as the model weight saving directory.

IV. Preparation: Understanding the Data from Repo

Data Set: The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)

• 12 Actors & 12 Actresses recorded speech and song version respectively.
• Actor no.18 does not have song version data.
• Emotion Disgust, Neutral and Surprised are not included in the song version data.

Total Class:

Emotion	Speech Sample Count	Song Sample Count	Summed Count
Neutral	96	92	188
Calm	192	184	376
Happy	192	184	376
Sad	192	184	376
Angry	192	184	376
Fearful	192	184	376
Disgust	192	0	192
Surprised	192	0	192
Total	1440	1012	2452

Sample Distribution:

• Originally, there are 16 target classes (8 emotions and each emotion split to male and female.) in total for 1440 samples (Speech Only). The author removed the disgust, surprised and neutral from both gender which reduced the target classes to 10.

V.Preparation: Understanding the Model

Model Architecture:

# Model 
model = Sequential()
model.add(Conv1D(256, 8, padding='same',input_shape=(X_train.shape[1],1))) #1
model.add(Activation('relu'))
model.add(Conv1D(256, 8, padding='same')) #2
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.25))
model.add(MaxPooling1D(pool_size=(8)))
model.add(Conv1D(128, 8, padding='same')) #3
model.add(Activation('relu')) 
model.add(Conv1D(128, 8, padding='same')) #4
model.add(Activation('relu'))
model.add(Conv1D(128, 8, padding='same')) #5
model.add(Activation('relu'))
model.add(Conv1D(128, 8, padding='same')) #6
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.25))
model.add(MaxPooling1D(pool_size=(8)))
model.add(Conv1D(64, 8, padding='same')) #7
model.add(Activation('relu'))
model.add(Conv1D(64, 8, padding='same')) #8
model.add(Activation('relu'))
model.add(Flatten())
model.add(Dense(target_class)) #9
model.add(Activation('softmax'))
opt = keras.optimizers.SGD(lr=0.0001, momentum=0.0, decay=0.0, nesterov=False)

VI. Project Summary

• Please refers to my Blog on Medium: Speech Emotion Recognition with Convolution Neural Network