The proposed approach, which leverages pre-trained audio models and Low-Rank Adaptation (LoRA) fine-tuning, can be effectively extended to other audio-related tasks such as audio classification and audio event detection by adapting the model architecture and training strategies to the specific requirements of these tasks.
Model Adaptation: For audio classification tasks, the model can be fine-tuned to categorize audio clips into predefined classes. This can be achieved by modifying the output layer to match the number of classes in the classification task and employing appropriate loss functions, such as categorical cross-entropy. The use of pre-trained models like BEATs and CED, which are designed for audio tagging, can provide a strong foundation for feature extraction, enhancing the model's ability to generalize across different audio categories.
Data Augmentation: Similar to the SpecAugment strategy used in anomalous sound detection, audio classification and event detection can benefit from diverse data augmentation techniques. Techniques such as time stretching, pitch shifting, and adding background noise can help create a more robust training dataset, improving the model's performance in real-world scenarios.
Transfer Learning: The principles of transfer learning can be applied, where models pre-trained on large audio datasets are fine-tuned on smaller, task-specific datasets. This approach can significantly reduce the amount of labeled data required for training, making it feasible to deploy models in scenarios where labeled data is scarce.
Multi-task Learning: The architecture can be designed to support multi-task learning, where the model is trained simultaneously on multiple audio-related tasks. This can enhance the model's ability to learn shared representations, improving performance across all tasks, including audio classification and event detection.
By leveraging these strategies, the proposed approach can be adapted to various audio-related tasks, enhancing its applicability and effectiveness in diverse audio processing scenarios.