YamNet transfer learning

Based on: Kaggle dataset

Let the results speak

accuracy 73%

precision	recall	f1-score	support
Sound_Drum	0.80	0.79	0.80
Sound_Guitar	0.94	0.67	0.78
Sound_Piano	0.68	0.93 0.78	1139
Sound_Violin	0.09	0.11	0.10
macro avg	0.63	0.62	0.61
weighted avg	0.77	0.73	0.73

epoch loss	categorical accuracy

---

About Dataset

The Musical Instruments Sound Dataset on Kaggle contains over 3,000 audio samples of notes from 11 musical instruments, recorded at a 16-bit/44.1 kHz sampling rate. The 2-4 second WAV format clips are labeled with the instrument class and split into training and test subsets. This collection can be used to develop and evaluate machine learning models for automatic musical instrument recognition.

---

Project Description

Transfer Learning on YamNet

What is YamNet?

YamNet is a Convolutional neural network that is specifically designed for audio classification. It is based on the MobileNetV1 architecture, which is a lightweight and efficient architecture that is well- suited for mobile devices. YamNet uses a mel-spectrogram as input, which is a representation of the audio signal that is frequency-aware. The mel-spectrogram is then passed through the YamNet network, which outputs a 1024-dimensional embedding.

classifier Architecture

my_model = Sequential([
# Dense(1024, activation='relu',),
keras.layers.Dense(128, input_shape=(1024,), activation="relu", kernel_regularizer=keras.regulari
keras.layers.Dropout(0.2),
Dense(4, activation='softmax'),
])

Big twist

I used YamNet embedding to simplify task and just train the classifier layers and my whole computations and train time drop reduced by 87%