Audio Retrieval with WavText5K and CLAP Training

TL;DR

This paper introduces WavText5K, a new web audio-text dataset, and a contrastive learning framework with dual audio encoders that significantly improve audio-text retrieval performance over previous methods.

Contribution

The paper presents a novel web audio-text dataset and a dual-encoder retrieval framework that enhances alignment between audio and text content, outperforming existing state-of-the-art models.

Findings

WavText5K dataset improves retrieval performance compared to other datasets.

Dual audio encoders effectively handle variable-length audio content.

Achieved 2-23% improvements over state-of-the-art on benchmark datasets.

Abstract

Audio-Text retrieval takes a natural language query to retrieve relevant audio files in a database. Conversely, Text-Audio retrieval takes an audio file as a query to retrieve relevant natural language descriptions. Most of the literature train retrieval systems with one audio captioning dataset, but evaluating the benefit of training with multiple datasets is underexplored. Moreover, retrieval systems have to learn the alignment between elaborated sentences describing audio content of variable length ranging from a few seconds to several minutes. In this work, we propose a new collection of web audio-text pairs and a new framework for retrieval. First, we provide a new collection of about five thousand web audio-text pairs that we refer to as WavText5K. When used to train our retrieval system, WavText5K improved performance more than other audio captioning datasets. Second, our framework learns to connect language and audio content by using a text encoder, two audio encoders, and a contrastive learning objective. Combining both audio encoders helps to process variable length audio. The two contributions beat state of the art performance for AudioCaps and Clotho on Text-Audio retrieval by a relative 2% and 16%, and Audio-Text retrieval by 6% and 23%.