DeepAI AI Chat
Log In Sign Up

Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning

by   Sungkyun Chang, et al.

Most of existing audio fingerprinting systems have limitations to be used for high-specific audio retrieval at scale. In this work, we generate a low-dimensional representation from a short unit segment of audio, and couple this fingerprint with a fast maximum inner-product search. To this end, we present a contrastive learning framework that derives from the segment-level search objective. Each update in training uses a batch consisting of a set of pseudo labels, randomly selected original samples, and their augmented replicas. These replicas can simulate the degrading effects on original audio signals by applying small time offsets and various types of distortions, such as background noise and room/microphone impulse responses. In the segment-level search task, where the conventional audio fingerprinting systems used to fail, our system using 10x smaller storage has shown promising results. Our code and dataset will be available.


page 1

page 2

page 3

page 4


Contrastive Unsupervised Learning for Audio Fingerprinting

The rise of video-sharing platforms has attracted more and more people t...

Attention-Based Audio Embeddings for Query-by-Example

An ideal audio retrieval system efficiently and robustly recognizes a sh...

Language-Based Audio Retrieval with Converging Tied Layers and Contrastive Loss

In this paper, we tackle the new Language-Based Audio Retrieval task pro...

Robust and lightweight audio fingerprint for Automatic Content Recognition

This research paper presents a novel audio fingerprinting system for Aut...

A Review of Audio Features and Statistical Models Exploited for Voice Pattern Design

Audio fingerprinting, also named as audio hashing, has been well-known a...

Crowdsourcing and Evaluating Text-Based Audio Retrieval Relevances

This paper explores grading text-based audio retrieval relevances with c...

Multipath-enabled private audio with noise

We address the problem of privately communicating audio messages to mult...