Tencent Music Entertainment Technology (shenzhen) Co., Ltd.

Dong Xu

A method for accompaniment purity class evaluation and related devices are provided. Multiple first accompaniment data and a label corresponding to each of the multiple first accompaniment data are obtained, the label being used to indicate that corresponding first accompaniment data is pure instrumental accompaniment data or instrumental accompaniment data with background noise. An audio feature of each of the multiple first accompaniment data is extracted. Model training is performed according to the audio feature of each of the multiple first accompaniment data and the label corresponding to each of the multiple 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 multiple first accompaniment data and the label corresponding to each of the multiple first accompaniment data.

Method for accompaniment purity class evaluation and related devices

An accompaniment classification method and apparatus is provided. The method includes the following. A first type of audio features of a target accompaniment is obtained (S, S). Data normalization is performed on each kind of audio features in the first type of audio features of the target accompaniment to obtain a first feature-set of the target accompaniment and the first feature-set is input into a first classification model for processing (S, S). A first probability value output by the first classification model for the first feature-set is obtained (S, S). An accompaniment category of the target accompaniment is determined to be a first category of accompaniments when the first probability value is greater than a first classification threshold (S). The accompaniment category of the target accompaniment is determined to be other categories of accompaniments when the first probability value is less than or equal to the first classification threshold.

Accompaniment classification method and apparatus

A method and a device for audio repair and a readable storage medium are provided. The method includes the following. Multiple audio frames are sequentially inputted into a cache module, where the cache module is sequentially composed of multiple processing units, and a processing unit located at a center of the multiple processing units is a center processing unit (). At least one audio frame contained in the center processing unit is assigned as a target frame (). A noise point presented as a short-term high-energy pulse in the target frame is detected according to audio characteristics of the multiple audio frames in the cache module (). The target frame is repaired to remove the noise point in the target frame ().

Method and device for audio repair and readable storage medium

Provided is a sound quality detection method, including: acquiring a plurality of audio files to be detected, wherein the plurality of audio files are homologous audio files; acquiring at least one audio feature of each of the plurality of audio files by performing feature extraction on the audio file, and generating a correspondence list between the at least one audio feature of each of the plurality of audio files and an audio file identifier; and determining, using a sound quality detection model, a sound quality score of each of the plurality of audio files based on the correspondence list between the at least one audio feature of each of the plurality of audio files and the audio file identifier, wherein the sound quality detection model is configured to detect sound quality of homologous audio files.

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Guangdong, China

Dong Xu