Anime Audio Retrieval Based on Audio Separation and Feature Recognition

This paper proposes an anime audio retrieval method based on audio separation and feature recognition techniques, aiming to help users conveniently locate their desired audio segments and enhance the overall user experience. Additionally, by establishing an audio fingerprint database and a corresponding copyright information management system, it becomes possible to track and manage the audio content within anime, effectively preventing piracy and unauthorized use, thereby improving the management and protection of audio resources. Traditional methods for anime audio feature recognition suffer from issues like low efficiency and subjective factors. In contrast, the proposed approach overcomes these limitations by automatically separating and extracting audio fingerprints from different audio sources within anime and creating an anime audio fingerprint database for fast retrieval. The paper utilizes an improved audio separation model based on the efficient channel attention mechanism to separate the anime audio. Subsequently, feature recognition is performed on the separated anime audio, employing a contrastive learning-based audio fingerprint retrieval method for anime audio fingerprinting. Experimental results demonstrate that the proposed algorithm effectively alleviates the issue of poor audio separation performance in anime audio, while also improving retrieval efficiency and accuracy, meeting the demands for anime audio content retrieval.