AMG-Embedding: A Self-Supervised Embedding Approach for Audio Identification

Audio Identification aims to precisely retrieve exact matches from a vast music repository through a query audio snippet. The need for specificity and granularity has traditionally led to representing music audio using numerous short fixed-duration overlapped segment/shingle features in fingerprinting approaches. However, fingerprinting imposes constraints on scalability and efficiency, as hundreds or even thousands of embeddings are generated to represent a music audio. In this paper, we present an innovative self-supervised approach called Angular Margin Guided Embedding (AMG-Embedding). AMG-Embedding is built on a traditional fingerprinting encoder and aims to represent variable-duration non-overlapped segments as embeddings through a two-stage embedding and class-level learning process. AMG-Embedding significantly reduces the number of generated embeddings while achieving high-specific fragment-level audio identification simultaneously. Experimental results demonstrate that AMG-Embedding achieves retrieval accuracy comparable to the based fingerprinting approach while consuming less than 1/10th of its storage and retrieval time. The efficiency gains of our approach position it as a promising solution for scalable and efficient audio identification systems.