# Memo2496: Expert-Annotated Dataset and Dual-view Adaptive Framework for Music Emotion Recognition

arXiv: https://arxiv.org/abs/2512.13998 · HTML: https://mlsanity.com/paper/2512.13998/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 87 references that are in the map.
- First posted on arXiv 16 Dec 2025.

## Builds on (most cited first, 20 of 20)

- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
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- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation](https://mlsanity.com/paper/2108.01374/) (2021)
- [Convolutional Recurrent Neural Networks for Music Classification](https://mlsanity.com/paper/1609.04243/) (2016)
- [MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer](https://mlsanity.com/paper/1809.07600/) (2018)
- [SECap: Speech Emotion Captioning with Large Language Model](https://mlsanity.com/paper/2312.10381/) (2023)
- [Learning to Generate Music With Sentiment](https://mlsanity.com/paper/2103.06125/) (2021)
- [Are We There Yet? A Brief Survey of Music Emotion Prediction Datasets, Models and Outstanding Challenges](https://mlsanity.com/paper/2406.08809/) (2024)
- [A Efficient Multimodal Framework for Large Scale Emotion Recognition by Fusing Music and Electrodermal Activity Signals](https://mlsanity.com/paper/2008.09743/) (2020)
- [Hybrid Data Augmentation and Deep Attention-based Dilated Convolutional-Recurrent Neural Networks for Speech Emotion Recognition](https://mlsanity.com/paper/2109.09026/) (2021)
- [ADFF: Attention Based Deep Feature Fusion Approach for Music Emotion Recognition](https://mlsanity.com/paper/2204.05649/) (2022)
- [MERGE -- A Bimodal Audio-Lyrics Dataset for Static Music Emotion Recognition](https://mlsanity.com/paper/2407.06060/) (2024)
- [Semi-Supervised Cognitive State Classification from Speech with Multi-View Pseudo-Labeling](https://mlsanity.com/paper/2409.16937/) (2024)
- [Personalized Dynamic Music Emotion Recognition with Dual-Scale Attention-Based Meta-Learning](https://mlsanity.com/paper/2412.19200/) (2024)
- [GlobalMood: A cross-cultural benchmark for music emotion recognition](https://mlsanity.com/paper/2505.09539/) (2025)
- [MEEG and AT-DGNN: Improving EEG Emotion Recognition with Music Introducing and Graph-based Learning](https://mlsanity.com/paper/2407.05550/) (2024)
- [Uncertainty Estimation in the Real World: A Study on Music Emotion Recognition](https://mlsanity.com/paper/2501.11570/) (2025)

## Abstract (arXiv)

Music Emotion Recognition (MER) is constrained by limited expert annotations and the need to establish robustness across heterogeneous corpora. Memo2496 supplies a reproducible dataset of 2,496 instrumental tracks with continuous valence-arousal labels from 30 certified music specialists, supported by interface familiarisation and duplicate-track intra-annotator calibration in a normalised circular domain. We also introduce the Dual-view Adaptive Music Emotion Recogniser (DAMER), a general framework evaluated on Memo2496 and two external datasets. DAMER integrates Dual-Stream Attention Fusion (DSAF) for token-level bidirectional interaction between Mel spectrograms and cochleagrams, Progressive Confidence Labelling (PCL) for curriculum-based pseudo-labels using temperature scheduling and Jensen-Shannon divergence, and Style-Anchored Memory Learning (SAML), whose labelled contrastive queue regularises same-emotion embeddings across acoustically varied samples. The primary evaluation follows the binary MER protocol used on PMEmo and 1000songs, while a supplementary continuous regression study demonstrates direct use of Memo2496 segment-level valence and arousal scores. Experiments on Memo2496, 1000songs, and PMEmo show that DAMER achieves the highest arousal accuracy among compared methods on Memo2496 and 1000songs and the highest valence accuracy on PMEmo, while remaining competitive for PMEmo arousal. Ablations and diagnostics validate each module. The dataset and source code are publicly available.