How to get better accuracy?

You can further tune your neural network to achieve higher accuracy and better generalization on the dataset by systematically experimenting with these techniques and monitoring the model’s performance:

• Adding Layers: Increasing the depth of the network by adding more layers allows the model to capture more complex patterns in the data, potentially improving its ability to generalize.
• Changing Layer Sizes: Adjusting the number of neurons in each layer can control the model’s capacity. Increasing the number of neurons may enable the network to learn more intricate relationships in the data, while reducing the number can help prevent overfitting.
• Changing Activation Functions: Different activation functions affect how information flows through the network. Experimenting with alternatives like ReLU, Leaky ReLU, or ELU can help improve the model’s ability to capture nonlinearities in the data.
• Regularization: Techniques such as dropout, L1, or L2 regularization help prevent overfitting by introducing constraints on the network’s parameters. Regularization encourages the model to learn simpler representations, leading to better generalization performance.
• Batch Normalization: Adding batch normalization layers helps stabilize and accelerate the training process by normalizing the activations of each layer. This can lead to faster convergence and improved performance, especially in deeper networks.

With abundant audio data available, analyzing and classifying it presents a significant challenge due to the complexity and variability of sound. This is where transfer learning comes in, offering a solution to tackle audio classification tasks with greater efficiency and accuracy. In this article, we will explore the application of transfer learning for audio classification, specifically focusing on using the YAMNet model to classify animal sounds.