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Audio fingerprinting systems must efficiently and robustly identify querysnippets in an extensive database. To this end, state-of-the-art systems usedeep learning to generate compact audio fingerprints. These systems deployindexing methods, which quantize fingerprints to hash codes in an unsupervisedmanner to expedite the search. However, these methods generate imbalanced hashcodes, leading to their suboptimal performance. Therefore, we propose aself-supervised learning framework to compute fingerprints and balanced hashcodes in an end-to-end manner to achieve both fast and accurate retrievalperformance. We model hash codes as a balanced clustering process, which weregard as an instance of the optimal transport problem. Experimental resultsindicate that the proposed approach improves retrieval efficiency whilepreserving high accuracy, particularly at high distortion levels, compared tothe competing methods. Moreover, our system is efficient and scalable incomputational load and memory storage.

Simultaneously Learning Robust Audio Embeddings and balanced Hash codes for Query-by-Example