Audio Atlas: Visualizing and Exploring Audio Datasets

TL;DR

Audio Atlas is an interactive web tool that visualizes and explores audio datasets using text-audio embeddings, enabling users to identify patterns and outliers efficiently.

Contribution

It introduces a novel visualization system for audio data that combines contrastive embeddings, a vector database, and dynamic visualization for better dataset understanding.

Findings

Effective visualization of diverse audio datasets

Facilitates pattern and outlier detection in audio data

Open-source implementation available

Abstract

We introduce Audio Atlas, an interactive web application for visualizing audio data using text-audio embeddings. Audio Atlas is designed to facilitate the exploration and analysis of audio datasets using a contrastive embedding model and a vector database for efficient data management and semantic search. The system maps audio embeddings into a two-dimensional space and leverages DeepScatter for dynamic visualization. Designed for extensibility, Audio Atlas allows easy integration of new datasets, enabling users to better understand their audio data and identify both patterns and outliers. We open-source the codebase of Audio Atlas, and provide an initial implementation containing various audio and music datasets.