Method for accompaniment purity class evaluation and related devices

An electronic device, comprising a processor and a memory, wherein the processor is coupled with the memory, the memory is configured to store computer programs, the computer programs comprise program instructions, and the processor is configured to invoke the program instructions to:

• obtain a plurality of first accompaniment data and a label corresponding to each of the plurality of first accompaniment data, the label corresponding to each of the plurality of first accompaniment data being used to indicate that corresponding first accompaniment data is pure instrumental accompaniment data or instrumental accompaniment data with background noise;
• extract an audio feature of each of the plurality of first accompaniment data;
• and
• perform model training according to the audio feature of each of the plurality of first accompaniment data and the label corresponding to each of the plurality of first accompaniment data, to obtain a neural network model for accompaniment purity class evaluation, a model parameter of the neural network model being determined according to an association relationship between the audio feature of each of the plurality of first accompaniment data and the label corresponding to each of the plurality of first accompaniment data.

A non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium is configured to store computer programs, the computer programs comprise program instructions which, when executed by a processor, are operable with the processor to:

• obtain data to-be-tested, the data to-be-tested comprising accompaniment data;
• extract an audio feature of the accompaniment data;
• and
• input the audio feature into a neural network model, to obtain a purity class evaluation result of the accompaniment data, the evaluation result being used to indicate that the data to-be-tested is pure instrumental accompaniment data or instrumental accompaniment data with background noise, the neural network model being obtained through training according to a plurality of samples, the plurality of samples comprising an audio feature of each of a plurality of accompaniment data and a label corresponding to each of the plurality of accompaniment data, a model parameter of the neural network model being determined according to an association relationship between the audio feature of each of the plurality of accompaniment data and the label corresponding to each of the plurality of accompaniment data.