Thaat and Raga Forest (TRF) Dataset

Instructions for Utilizing the "Thaat and Raga Forest (TRF) Dataset":

1. Dataset Access and Preparation:

   • Obtain access to the TRF Dataset, ensuring compliance with any licensing or usage terms associated with the dataset.
   • Download the dataset files provided in mp3 and wav formats, ensuring they are stored in a structured directory hierarchy for easy access and organization.

2. Data Exploration:

   • Familiarize yourself with the dataset structure, including the directory organization and file naming conventions.
   • Explore the dataset metadata, if available, to understand the attributes and annotations associated with each recording, such as Thaat, Raga, artist information, and genre tags.

3. Data Preprocessing:

   • Preprocess the audio data as needed for your specific analysis tasks, which may include:
     • Converting audio files to a consistent format (e.g., wav) for compatibility.
     • Resampling audio files to a consistent sample rate if necessary.
     • Segmenting audio recordings into smaller chunks or frames for model input.
     • Extracting relevant features from audio signals, such as spectrograms, Mel-frequency cepstral coefficients (MFCCs), or other time-frequency representations.
     • Normalizing audio amplitude levels to ensure consistency across recordings.

4. Model Development:

   • Design and implement Deep Learning models tailored to your research objectives, such as Thaat and Raga classification, sentiment analysis, or recommendation systems.
   • Experiment with various neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), or Transformer models, depending on the nature of the audio analysis task.
   • Incorporate appropriate loss functions and evaluation metrics for assessing model performance, considering factors such as accuracy, precision, recall, and F1-score.

5. Training and Evaluation:

   • Split the dataset into training, validation, and test sets to train and evaluate your models effectively.
   • Train the Deep Learning models using the training data while monitoring performance on the validation set to prevent overfitting.
   • Fine-tune model hyperparameters and architecture based on validation performance, employing techniques like cross-validation if applicable.
   • Evaluate the trained models on the test set to assess their generalization performance and robustness.

6. Analysis and Interpretation:

   • Analyze the model predictions on the test set to gain insights into the effectiveness of the Deep Learning approach for Thaat and Raga identification.
   • Interpret model outputs and errors to understand the challenges and limitations of computational music analysis in the context of Indian Classical Music.
   • Explore potential applications of the trained models, such as sentiment-based music recommendation, automated playlist generation, or music similarity detection.

7. Documentation and Reporting:

   • Document the experimental setup, including dataset preprocessing steps, model architectures, training configurations, and evaluation results.
   • Report key findings, including model performance metrics, insights gained from the analysis, and potential areas for future research.
   • Share the findings and codebase with the research community through publications, presentations, or open-source repositories to foster collaboration and knowledge dissemination.