# Regression-based music emotion prediction using triplet neural networks

arXiv: https://arxiv.org/abs/2001.09988 · HTML: https://mlsanity.com/paper/2001.09988/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2020.

## Built on by (most cited first, 9 of 9)

- [Generating Lead Sheets with Affect: A Novel Conditional seq2seq Framework](https://mlsanity.com/paper/2104.13056/) (2021)
- [Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition](https://mlsanity.com/paper/2104.06517/) (2021)
- [Are We There Yet? A Brief Survey of Music Emotion Prediction Datasets, Models and Outstanding Challenges](https://mlsanity.com/paper/2406.08809/) (2024)
- [Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses](https://mlsanity.com/paper/2202.10453/) (2022)
- [aiSTROM -- A roadmap for developing a successful AI strategy](https://mlsanity.com/paper/2107.06071/) (2021)
- [Emotion Manipulation Through Music -- A Deep Learning Interactive Visual Approach](https://mlsanity.com/paper/2406.08623/) (2024)
- [A Study on the Data Distribution Gap in Music Emotion Recognition](https://mlsanity.com/paper/2510.04688/) (2025)
- [Words to Waves: Emotion-Adaptive Music Recommendation System](https://mlsanity.com/paper/2510.21724/) (2025)
- [Learning to Predict Performance-induced Emotion Differences in Classical Piano Music](https://mlsanity.com/paper/2607.28876/) (2026)

## Abstract (arXiv)

In this paper, we adapt triplet neural networks (TNNs) to a regression task, music emotion prediction. Since TNNs were initially introduced for classification, and not for regression, we propose a mechanism that allows them to provide meaningful low dimensional representations for regression tasks. We then use these new representations as the input for regression algorithms such as support vector machines and gradient boosting machines. To demonstrate the TNNs' effectiveness at creating meaningful representations, we compare them to different dimensionality reduction methods on music emotion prediction, i.e., predicting valence and arousal values from musical audio signals. Our results on the DEAM dataset show that by using TNNs we achieve 90% feature dimensionality reduction with a 9% improvement in valence prediction and 4% improvement in arousal prediction with respect to our baseline models (without TNN). Our TNN method outperforms other dimensionality reduction methods such as principal component analysis (PCA) and autoencoders (AE). This shows that, in addition to providing a compact latent space representation of audio features, the proposed approach has a higher performance than the baseline models.