Audio Fingerprinting with Holographic Reduced Representations

TL;DR

This paper introduces an audio fingerprinting approach using holographic reduced representations to significantly reduce stored fingerprints while maintaining high accuracy and time resolution.

Contribution

It presents a novel HRR-based aggregation method for audio fingerprints that reduces storage needs without sacrificing time resolution or accuracy.

Findings

Reduces number of fingerprints needed for high accuracy

Maintains time resolution with fewer fingerprints

Outperforms simple aggregation methods

Abstract

This paper proposes an audio fingerprinting model with holographic reduced representation (HRR). The proposed method reduces the number of stored fingerprints, whereas conventional neural audio fingerprinting requires many fingerprints for each audio track to achieve high accuracy and time resolution. We utilize HRR to aggregate multiple fingerprints into a composite fingerprint via circular convolution and summation, resulting in fewer fingerprints with the same dimensional space as the original. Our search method efficiently finds a combined fingerprint in which a query fingerprint exists. Using HRR's inverse operation, it can recover the relative position within a combined fingerprint, retaining the original time resolution. Experiments show that our method can reduce the number of fingerprints with modest accuracy degradation while maintaining the time resolution, outperforming simple decimation and summation-based aggregation methods.