# Content-based Representations of audio using Siamese neural networks

arXiv: https://arxiv.org/abs/1710.10974 · HTML: https://mlsanity.com/paper/1710.10974/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [CDPAM: Contrastive learning for perceptual audio similarity](https://mlsanity.com/paper/2102.05109/) (2021)
- [Introducing Auxiliary Text Query-modifier to Content-based Audio Retrieval](https://mlsanity.com/paper/2207.09732/) (2022)
- [SEAM: Shortcut-Aware Real-Time Detection of Scripted vs. Spontaneous Speech for Interview Guardrails](https://mlsanity.com/paper/2606.06837/) (2026)
- [Siamese Neural Network for Label-Efficient Critical Phenomena Prediction in 3D Percolation Models](https://mlsanity.com/paper/2507.14159/) (2025)

## Abstract (arXiv)

In this paper, we focus on the problem of content-based retrieval for audio, which aims to retrieve all semantically similar audio recordings for a given audio clip query. This problem is similar to the problem of query by example of audio, which aims to retrieve media samples from a database, which are similar to the user-provided example. We propose a novel approach which encodes the audio into a vector representation using Siamese Neural Networks. The goal is to obtain an encoding similar for files belonging to the same audio class, thus allowing retrieval of semantically similar audio. Using simple similarity measures such as those based on simple euclidean distance and cosine similarity we show that these representations can be very effectively used for retrieving recordings similar in audio content.