# MERGE -- A Bimodal Audio-Lyrics Dataset for Static Music Emotion Recognition

arXiv: https://arxiv.org/abs/2407.06060 · HTML: https://mlsanity.com/paper/2407.06060/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 54 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation](https://mlsanity.com/paper/2108.01374/) (2021)
- [Automatic tagging using deep convolutional neural networks](https://mlsanity.com/paper/1606.00298/) (2016)
- [Music Mood Detection Based On Audio And Lyrics With Deep Neural Net](https://mlsanity.com/paper/1809.07276/) (2018)
- [Transformer-based approach towards music emotion recognition from lyrics](https://mlsanity.com/paper/2101.02051/) (2021)
- [The Biased Journey of MSD_AUDIO.ZIP](https://mlsanity.com/paper/2308.16389/) (2023)

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

- [Memo2496: Expert-Annotated Dataset and Dual-view Adaptive Framework for Music Emotion Recognition](https://mlsanity.com/paper/2512.13998/) (2025)
- [Open-Qwen-Music: An Auditable Framework for LLM-Based Music Composition and Diffusion Rendering](https://mlsanity.com/paper/2609.31652/) (2026)
- [Reading the Mood: Emotion-Guided Book-to-Music Recommendation via CGANs and LLMs](https://mlsanity.com/paper/2610.06703/) (2026)

## Abstract (arXiv)

The Music Emotion Recognition (MER) field has seen steady developments in recent years, with contributions from feature engineering, machine learning, and deep learning. The landscape has also shifted from audio-centric systems to bimodal ensembles that combine audio and lyrics. However, a lack of public, sizable and quality-controlled bimodal databases has hampered the development and improvement of bimodal audio-lyrics systems. This article proposes three new audio, lyrics, and bimodal MER research datasets, collectively referred to as MERGE, which were created using a semi-automatic approach. To comprehensively assess the proposed datasets and establish a baseline for benchmarking, we conducted several experiments for each modality, using feature engineering, machine learning, and deep learning methodologies. Additionally, we propose and validate fixed train-validation-test splits. The obtained results confirm the viability of the proposed datasets, achieving the best overall result of 81.74% F1-score for bimodal classification.