# Text-to-Audio Grounding Based Novel Metric for Evaluating Audio Caption Similarity

arXiv: https://arxiv.org/abs/2210.06354 · HTML: https://mlsanity.com/paper/2210.06354/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 3 Oct 2022.

## Builds on (most cited first, 18 of 18)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016)
- [Clotho: An Audio Captioning Dataset](https://mlsanity.com/paper/1910.09387/) (2019)
- [Audio Captioning Transformer](https://mlsanity.com/paper/2107.09817/) (2021)
- [Audio Retrieval with Natural Language Queries](https://mlsanity.com/paper/2105.02192/) (2021)
- [Automated Audio Captioning with Recurrent Neural Networks](https://mlsanity.com/paper/1706.10006/) (2017)
- [Can Audio Captions Be Evaluated with Image Caption Metrics?](https://mlsanity.com/paper/2110.04684/) (2021)
- [Audio Caption: Listen and Tell](https://mlsanity.com/paper/1902.09254/) (2019)
- [Text-to-Audio Grounding: Building Correspondence Between Captions and Sound Events](https://mlsanity.com/paper/2102.11474/) (2021)
- [Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning](https://mlsanity.com/paper/1905.01077/) (2019)
- [Diverse Audio Captioning via Adversarial Training](https://mlsanity.com/paper/2110.06691/) (2021)
- [Automated Audio Captioning using Transfer Learning and Reconstruction Latent Space Similarity Regularization](https://mlsanity.com/paper/2108.04692/) (2021)
- [Towards duration robust weakly supervised sound event detection](https://mlsanity.com/paper/2101.07687/) (2021)
- [Crowdsourcing a Dataset of Audio Captions](https://mlsanity.com/paper/1907.09238/) (2019)
- [The NTT DCASE2020 Challenge Task 6 system: Automated Audio Captioning with Keywords and Sentence Length Estimation](https://mlsanity.com/paper/2007.00225/) (2020)
- [Enhancing Sound Texture in CNN-Based Acoustic Scene Classification](https://mlsanity.com/paper/1901.01502/) (2019)

## Built on by (most cited first, 1 of 1)

- [BRACE: A Benchmark for Robust Audio Caption Quality Evaluation](https://mlsanity.com/paper/2512.10403/) (2025)

## Abstract (arXiv)

Automatic Audio Captioning (AAC) refers to the task of translating an audio sample into a natural language (NL) text that describes the audio events, source of the events and their relationships. Unlike NL text generation tasks, which rely on metrics like BLEU, ROUGE, METEOR based on lexical semantics for evaluation, the AAC evaluation metric requires an ability to map NL text (phrases) that correspond to similar sounds in addition lexical semantics. Current metrics used for evaluation of AAC tasks lack an understanding of the perceived properties of sound represented by text. In this paper, wepropose a novel metric based on Text-to-Audio Grounding (TAG), which is, useful for evaluating cross modal tasks like AAC. Experiments on publicly available AAC data-set shows our evaluation metric to perform better compared to existing metrics used in NL text and image captioning literature.