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Music Genre Classifier

This project involves building a deep learning model to classify music genres from audio clips using TensorFlow and Keras. The classifier uses mel spectrograms as input features.

Requirements

• TensorFlow
• Keras
• numpy
• scipy
• matplotlib
• pydub

Setup

1. Dataset: Ensure your dataset is organized in directories by genre under ../DATASET/sesler/Data/genres_original/.

2. Preprocessing:

   • load_wav_16k_mono(filename): Loads a WAV file as mono 16kHz.
   • audio_to_melspectogram(wav, sr=16000, n_mels=128, n_fft=2048, hop_length=512): Converts audio to mel spectrogram.

3. Data Preparation:

   • Load and preprocess audio files from the dataset.
   • Concatenate and prepare datasets for training.

4. Model Training:

   • Define and train a Convolutional Neural Network (CNN) model using TensorFlow and Keras.
   • Model architecture:
     • Input layer: (28, 128, 1)
     • Convolutional layers with MaxPooling
     • Dense layers with dropout
     • Output layer with softmax activation (11 classes)
   • Optimizer: Adam with custom parameters.
   • Loss function: Categorical Crossentropy.

5. Model Evaluation:

   • Evaluate the model on test data.
   • Plot training history using matplotlib.
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6. Save Model:

   • Save the trained model as Music_Classifier.h5.

7. Audio Prediction:

   • Use split_audio(file_path) to split an MP3 file into chunks.
   • Preprocess chunks into mel spectrograms for prediction.
   • Load the saved model and predict genres for each chunk.

Usage

• Ensure dependencies are installed (pip install -r requirements.txt).
• Run scripts sequentially for dataset preparation, model training, and audio prediction.

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