The language of sound search: Examining User Queries in Audio Search Engines

TL;DR

This paper analyzes user search queries in sound search engines through surveys and log data, revealing user preferences and behaviors to improve text-based audio retrieval systems.

Contribution

It introduces a new dataset of user queries and provides insights into user behavior, addressing gaps in current research on real-world audio search queries.

Findings

Survey queries are longer than log queries, indicating a preference for detailed descriptions.

Most queries are keyword-based, with few full sentences.

Key factors include sound source, usage, location, and number of sources.

Abstract

This study examines textual, user-written search queries within the context of sound search engines, encompassing various applications such as foley, sound effects, and general audio retrieval. Current research inadequately addresses real-world user needs and behaviours in designing text-based audio retrieval systems. To bridge this gap, we analysed search queries from two sources: a custom survey and Freesound website query logs. The survey was designed to collect queries for an unrestricted, hypothetical sound search engine, resulting in a dataset that captures user intentions without the constraints of existing systems. This dataset is also made available for sharing with the research community. In contrast, the Freesound query logs encompass approximately 9 million search requests, providing a comprehensive view of real-world usage patterns. Our findings indicate that survey queries are generally longer than Freesound queries, suggesting users prefer detailed queries when not limited by system constraints. Both datasets predominantly feature keyword-based queries, with few survey participants using full sentences. Key factors influencing survey queries include the primary sound source, intended usage, perceived location, and the number of sound sources. These insights are crucial for developing user-centred, effective text-based audio retrieval systems, enhancing our understanding of user behaviour in sound search contexts.