A deep learning model for classifying musical notes based on their pitch and length. This model achieves remarkable performance on the dataset, with a test accuracy of 99.66%.
Model Overview
Architecture
This model is built using a Convolutional Neural Network (CNN) architecture with the following features:
Input size: (64, 64, 1)
Number of parameters: 696,255
Layers include:
Multiple Conv2D and BatchNormalization layers for feature extraction
GlobalAveragePooling2D and Dense layers for classification
Regularization via Dropout layers
Output: 85 classes, representing combinations of pitch and note length.
Dataset
Original dataset size: 1,785 samples
Augmented dataset size: 71,400 samples
Total size: 73,185 samples
Labels include various combinations of pitch (A3, B4, C6, etc.) and note lengths (16th, quarter, whole, etc.).
Training Details
Optimizer: Adam
Loss function: Categorical Crossentropy
Epochs: 400+
Batch size: Optimized for balanced training speed and accuracy
Final evaluation results:
Test Loss: 0.1286
Test Accuracy: 99.66%
Examples of Feature Detection
Filter Visualization
Filters from the first convolutional layers demonstrate the features captured by the network.
Filter 1
Filter 2
Filter 3
Filter 4
Training and Validation Loss
The following graph shows the training and validation loss during model training:
