# Improving automated segmentation of radio shows with audio embeddings

arXiv: https://arxiv.org/abs/2002.05194 · HTML: https://mlsanity.com/paper/2002.05194/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2020.

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

- [Towards Multi-Level Transcript Segmentation: LoRA Fine-Tuning for Table-of-Contents Generation](https://mlsanity.com/paper/2601.02128/) (2026)
- [Advancing Topic Segmentation of Broadcasted Speech with Multilingual Semantic Embeddings](https://mlsanity.com/paper/2409.06222/) (2024)
- [Reading Between the Waves: Robust Topic Segmentation Using Inter-Sentence Audio Features](https://mlsanity.com/paper/2602.06647/) (2026)
- [Reusing Latent Speech Representations for Query-Conditioned Topic Localization in Transcripts](https://mlsanity.com/paper/2609.21844/) (2026)

## Abstract (arXiv)

Audio features have been proven useful for increasing the performance of automated topic segmentation systems. This study explores the novel task of using audio embeddings for automated, topically coherent segmentation of radio shows. We created three different audio embedding generators using multi-class classification tasks on three datasets from different domains. We evaluate topic segmentation performance of the audio embeddings and compare it against a text-only baseline. We find that a set-up including audio embeddings generated through a non-speech sound event classification task significantly outperforms our text-only baseline by 32.3% in F1-measure. In addition, we find that different classification tasks yield audio embeddings that vary in segmentation performance.