It is recommended to use light-second audio splitting to separate vocals online and separate background music and vocals very quickly.

The AI ??principle of vocal separation involves using deep learning models to extract and separate specific vocals from mixed audio. The following is a brief description of the process:

Data preparation: A large amount of audio data containing human voices and background sounds is collected, which is used to train the deep learning model.

Label data: Label the training data, indicating the human voice and background sound at each time point. This provides the target information required for supervised learning.

Deep learning model: Using deep learning structures such as convolutional neural networks (CNN), the model is able to separate human voices by learning the characteristics of the input audio. Recurrent Neural Networks (RNN) can handle the temporal nature of audio.

Feature extraction: The model extracts spectral and time domain features in audio data through structures such as convolutional layers, which helps distinguish human voices from other sounds.

Training: Train the model on labeled data, adjusting model parameters to minimize the difference between predicted and actual labels. Loss functions and optimization algorithms play a key role here.

Inference: After training, the model can be used to infer new audio data. The model predicts vocals and background sounds at each time point through forward propagation.

Optimization and adjustment: Optimization and adjustment based on model performance may require hyperparameter tuning or the use of more complex network structures.

Application: The trained model can be used in a variety of applications, including speech recognition, audio editing, and speech enhancement, to improve the accuracy and quality of these tasks.

In general, the AI ??principle of human voice separation is based on deep learning technology, and through the process of model training and inference, the goal of separating human voices from mixed audio is achieved.