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

arXiv: https://arxiv.org/abs/2410.08324 · HTML: https://mlsanity.com/paper/2410.08324/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 10 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [Clotho: An Audio Captioning Dataset](https://mlsanity.com/paper/1910.09387/) (2019)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [Audio Retrieval with Natural Language Queries: A Benchmark Study](https://mlsanity.com/paper/2112.09418/) (2021)
- [A Prompt Log Analysis of Text-to-Image Generation Systems](https://mlsanity.com/paper/2303.04587/) (2023)
- [Language-based Audio Retrieval Task in DCASE 2022 Challenge](https://mlsanity.com/paper/2209.09967/) (2022)
- [On The Open Prompt Challenge In Conditional Audio Generation](https://mlsanity.com/paper/2311.00897/) (2023)

## Built on by (most cited first, 3 of 3)

- [Omni-Embed-Audio: Leveraging Multimodal LLMs for Robust Audio-Text Retrieval](https://mlsanity.com/paper/2604.18360/) (2026)
- [An Evaluation Framework for Structured Audio Captions Validated by Controlled Perturbations](https://mlsanity.com/paper/2607.21424/) (2026)
- [CORA: A Protocol for Diagnosing Boundary Robustness in Text-to-Audio Retrieval under Query Reformulations](https://mlsanity.com/paper/2609.33433/) (2026)

## Abstract (arXiv)

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.