Feature Encoding Model for Audio Determination
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Solution Overview
Problem
Existing methods for determining whether audio recordings are the same face challenges due to the rich variations in musical compositions, such as changes in tune, timbre, tempo, structure, melody, and lyrics, leading to difficulties in accurately identifying feature vectors.
Innovation Solution
A method for generating a feature encoding model that involves acquiring sample audios with category labels, extracting audio features, encoding these features, and updating the model's parameters based on a target loss function to reduce intra-class differences and increase inter-class differences, thereby improving the identifiability of feature vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional feature extraction methods are used for audio determination, then the process is simple, but the identifiability of feature vectors is poor due to rich variations in musical compositions
Solution Approach 1:
The patent applies preliminary action by pre-training an encoding model using a large amount of audio data before using it for actual audio determination. The model is trained offline to learn robust feature representations that can handle various musical variations, so that when the model is deployed for audio matching, it already possesses the capability to extract discriminative features despite the complexity of the training process.
2Reliability
If a simple feature extraction method is used, then the model is easy to implement, but it cannot distinguish between different audio recordings with significant variations
Solution Approach 1:
The patent employs preliminary action by conducting extensive pre-training of the encoding model using diverse audio data before deployment. This pre-training phase allows the model to learn robust feature representations that generalize well to different musical variations, ensuring high retrieval accuracy while keeping the actual audio determination process relatively simple.
Solution Approach 2:
The patent implements feedback through the use of loss functions during the training process that guide the model to minimize prediction errors. The feedback mechanism adjusts model parameters based on the difference between predicted and actual outcomes, enabling the model to progressively improve its ability to distinguish between different audio recordings while maintaining computational efficiency.
Data Source
AI summary
The disclosure relates to a method for generating a feature encoding model. The method includes: acquiring a plurality of sample audios marked with category labels; extracting audio features of the plurality of sample audios; encoding the audio features of the plurality of sample audios by the feature encoding model to obtain a plurality of encoding vectors of the plurality of sample audios, and performing classification processing on the plurality of sample audios based on the plurality of encoding vectors to obtain category prediction values of the plurality of sample audios; and determining a target loss value of a target loss function based on the plurality of encoding vectors, the category prediction values of the plurality of sample audios and the category labels of the plurality of sample audios, and updating a parameter of the feature encoding model based on the target loss value-se as to obtain the trained feature encoding model.