Audio Atlas: Visualizing and Exploring Audio Datasets
This tool addresses the need for better audio data exploration for researchers and practitioners, though it is incremental as it applies existing embedding and visualization techniques to audio datasets.
The authors tackled the problem of exploring and analyzing audio datasets by introducing Audio Atlas, an interactive web application that visualizes audio data using text-audio embeddings and maps them into a 2D space, resulting in a tool that facilitates semantic search and pattern identification.
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.