Two-Stage Audio Classifier for Rap Genre Identification
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Solution Overview
Problem
Existing audio processing systems struggle to accurately classify new music genres, such as rap and a cappella, leading to false identification and audible artifacts when applying dialogue enhancement or intelligent equalization algorithms.
Innovation Solution
A two-stage audio classifier system is proposed, where the first stage uses existing classifiers to generate initial confidence scores, and the second stage employs new classifiers, specifically a rap music classifier, to refine the classification, thereby improving accuracy and minimizing disruptions to existing audio processing behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a general music classifier is used to identify audio content, then it can accurately classify mainstream music genres (pop, jazz, country, rock, latin), but it fails to accurately classify new music genres such as rap and a cappella, leading to false identification as speech
Solution Approach 1:
The audio classification system is segmented into two independent stages: a first stage using an original music classifier for mainstream genres, and a second stage using a newly trained classifier for emerging genres. Each stage operates with its own confidence score, allowing the system to handle different genre types with specialized classifiers rather than forcing a single classifier to cover all genres.
Solution Approach 2:
The system changes the parameter of classifier selection based on the audio content being processed. By training a new classifier specifically on emerging genres (rap, a cappella, electronic) while keeping the original classifier for mainstream genres, the system adapts its classification parameters to match the specific genre requirements, improving overall accuracy across diverse music types.
2Measurement precision
If a new classifier is trained to improve classification accuracy for new music genres, then accuracy for rap and a cappella improves, but it may disrupt existing audio processing behaviors and require extensive retuning of audio processing algorithms
Solution Approach 1:
A confidence score combination mechanism serves as an intermediary between the first classifier and the second classifier. The steering signal acts as a mediator that smoothly transitions between the confidence scores of both classifiers, allowing the new classifier to be integrated without completely replacing the original one. This gradual integration approach minimizes disruption to existing audio processing pipelines.
Solution Approach 2:
The system dynamically adjusts the weight of each classifier's confidence score based on the steering signal, which is calculated from the difference between the two confidence scores. This dynamic weighting allows the system to automatically adapt to different audio content types, applying the appropriate classifier weight without manual retuning for each scenario.
3Reliability
If dialogue enhancement algorithm is applied to rap music falsely identified as speech, then speech clarity is improved, but audible artifacts are introduced due to inappropriate processing of rhythmic talking
Solution Approach 1:
The system performs preliminary classification using both the original music classifier and the new classifier before applying any audio processing algorithms. By evaluating confidence scores from both classifiers and using a steering signal to determine the final classification, the system proactively identifies rap music as music rather than speech, preventing the inappropriate application of dialogue enhancement algorithms that would create audible artifacts.
Data Source
AI summary
A method of audio content identification includes using a two-stage classifier. The first stage includes previously-existing classifiers and the second stage includes a new classifier. The outputs of the first and second stages calculated over different time periods are combined to generate a steering signal. The final classification results from a combination of the steering signal and the outputs of the first and second stages. In this manner, a new classifier may be added without disrupting existing classifiers.