Robust Audio DNA-Based Method for OTT Content Recognition in Noisy Web Streaming Environments

With the proliferation of Over-The-Top (OTT) platforms via web browsers and mobile web apps, the demand for real-time copyright protection in web streaming environments has surged. However, unstructured noise generated when users consume web content in public environments (e.g., subways, cafes) increases the false positive rate of existing clean-audio-based identification systems and heavily burdens web server computations. This paper proposes a robust, audio DNA-based content recognition method capable of fast and accurate retrieval from large-scale media databases even in noisy web streaming environments. The proposed method extracts dual-stage (Coarse-Fine) features based on Mel-spectrograms at the client side and performs a highly efficient three-stage matching pipeline (Coarse Matching, Fine Matching, and Post-verification) at the web server side. Experimental results on 649 noisy audio samples demonstrated that applying web-optimized parameters (FFT length of 4096, Hop length of 1470) achieved a precision of 0.9965, a recall of 0.8814, and an F1-score of 0.9354. The proposed method significantly accelerates retrieval speed for large-scale web streaming data through binary hash matching in the Coarse stage while maintaining high accuracy, proving its effectiveness for real-time OTT copyright protection and web media monitoring systems.