How to separate vocals and accompaniment? It is recommended to use light-second audio splitting, fool-like operation, online separation of human voices, and extremely fast separation of background music and human voices.

Vocal separation is an audio processing technique designed to separate specific vocal parts from mixed audio. This is useful for applications such as speech recognition, speech enhancement, audio editing, etc. The application of AI in human voice separation usually involves deep learning and neural network technology. The following are the general principles of vocal separation:

Deep learning model: Use deep learning models such as Deep Neural Networks (DNN) or Convolutional Neural Networks (CNN). These models are able to learn complex feature representations that help separate human voices from mixed audio.

Training data: In order to train the model, a large amount of audio data containing human voices and background sounds is required. This data is used to train the model so that it learns to recognize the characteristics of human voices and other noise.

Labeled data: Training data usually requires labels, indicating which sounds are human voices and which are background noise at each time point. This helps the model learn the correct separation pattern.

Feature extraction: In deep learning models, convolutional layers are usually used to extract features in audio. These features may include spectral information, time domain information, etc., which help distinguish human voices from other sounds.

Recurrent Neural Networks (RNN): In audio processing, time series is very important because audio is a signal that changes over time. Recurrent neural network structures such as RNN can capture the timing information of audio signals and help better process audio data.

Loss function: During the training process, a loss function needs to be defined to measure the difference between the model output and the actual label. Common loss functions include the cross-entropy loss function.

Optimization algorithm: Adjust the model parameters through optimization algorithms such as gradient descent, so that the model can better separate human voices and background sounds.

Inference: After training, the model can be used for inference, that is, to separate human voices on new audio data. The inference stage typically uses forward propagation to predict the vocal and background sounds at each time point in the audio through the model.

The performance of human voice separation depends on many factors such as the quality of training data, model architecture, and parameter adjustment. In recent years, with the continuous development of deep learning technology, vocal separation has made significant progress in practical applications.