This repository contains implementations of various neural network architectures for audio classification and processing tasks, as well as other machine learning models such as Hidden Markov Models (HMMs) and Support Vector Machines (SVMs). Each model is designed to handle specific aspects of audio data, ranging from classification and recognition to synthesis and multi-band processing.

File: AudioCNN

• A CNN model designed for audio classification tasks.
• Architecture:
  • Two convolutional layers with ReLU activation and max pooling.
  • Two fully connected layers for classification.
• Input: Spectrogram or MFCC features with shape (batch_size, 1, 32, 32).

File: AudioRNN

• An RNN model using LSTM layers for speech recognition tasks.
• Architecture:
  • LSTM layers followed by a fully connected layer.
• Input: Audio feature sequences reshaped to (batch_size, sequence_length, 40).

File: AudioTransformer

• A Transformer model for speech recognition.
• Architecture:
  • Linear embedding layer.
  • Transformer encoder with multiple layers and heads.
  • Fully connected output layer.
• Input: Audio sequences reshaped to (sequence_length, batch_size, hidden_dim).

File: WaveNet

• A model based on WaveNet architecture for audio generation.
• Architecture:
  • Multiple dilated convolution layers.
  • Skip and residual connections.
  • Final convolution layer for output.
• Input: Raw audio waveforms.

File: AudioVAE

• A Variational Autoencoder for generating audio data.
• Architecture:
  • Encoder and decoder networks with linear layers and ReLU activation.
  • Latent space for encoding and reconstruction.
• Input: Flattened audio features.

Functions: train_hmm, predict_hmm

• Implementation of Gaussian Hidden Markov Models for sequential data.
• Uses hmmlearn library.
• Input: Sequential feature data.

Functions: train_svm, predict_svm

• Linear SVM classifier for audio classification tasks.
• Uses sklearn.svm.
• Input: Flattened audio features.

File: SubbandNN

• A neural network model for processing subband audio signals.
• Architecture:
  • Separate neural networks for each subband.
  • Linear layers with ReLU activation.
• Input: Subband features.

Function: evaluate_classification_model

• Evaluates the performance of classification models.
• Metrics: Accuracy, Precision, Recall, F1-Score.
• Input: Model, test data, and labels.

The main function provides example usage for the models:

• Instantiates CNN, RNN, and Transformer models.
• Evaluates each model using dummy data.

• Python 3.x
• PyTorch
• scikit-learn
• hmmlearn

1. Ensure all dependencies are installed.
2. Run the main function to test the models:

   python main.py

• Replace the dummy data in main with actual preprocessed audio data for real use cases.
• Adjust model parameters as needed based on the specific dataset and task.

• PyTorch Documentation
• hmmlearn Documentation
• scikit-learn Documentation