• Corpus ID: 245131041

Visualizing Ensemble Predictions of Music Mood

@article{Ye2021VisualizingEP,
  title={Visualizing Ensemble Predictions of Music Mood},
  author={Zelin Ye and Min Chen},
  journal={ArXiv},
  year={2021},
  volume={abs/2112.07627}
}
Music mood classification has been a challenging problem in comparison with some other classification problems (e.g., genre, composer, or period). One solution for addressing this challenging is to use an of ensemble machine learning models. In this paper, we show that visualization techniques can effectively convey the popular prediction as well as uncertainty at different music sections along the temporal axis, while enabling the analysis of individual ML models in conjunction with their… 

Figures from this paper

References

SHOWING 1-10 OF 76 REFERENCES
A framework for evaluating multimodal music mood classification
TLDR
Experimental results on a large data set of 18 mood categories show that combining lyrics and audio significantly outperformed systems using audio‐only features and automatic feature selection techniques were further proved to have reduced feature space.
Lyric Text Mining in Music Mood Classification
TLDR
Findings show patterns at odds with findings in previous studies: audio features do not always outperform lyrics features, and combining lyrics and audio features can improve performance in many mood categories, but not all of them.
Musical Texture and Expressivity Features for Music Emotion Recognition
TLDR
A set of novel emotionally-relevant audio features are presented to help improving the classification of emotions in audio music and developed a set of new algorithms to capture information related with musical texture and expressive techniques, the two most lacking concepts.
Multimodal Music Mood Classification Using Audio and Lyrics
TLDR
It is demonstrated that lyrics and audio information are complementary, and can be combined to improve a classification system, and integrating this in a multimodal system allows an improvement in the overall performance.
Music Genre Classification with Transformer Classifier
TLDR
A Transformer classifier is designed, Inspired by an advance in Natural Language Processing (NLP), that analyzes the relationship between different audio frames well and achieves better performance in Music Genre Classification.
Classification of musical genre: a machine learning approach
TLDR
This work investigates the impact of machine learning algorithms in the development of automatic music classification models aiming to capture genres distinctions by first creating a medium-sized collection of examples for widely recognized genres and then evaluating the performances of different learning algorithms.
Novel Audio Features for Music Emotion Recognition
TLDR
This work advances the music emotion recognition state-of-the-art by proposing novel emotionally-relevant audio features related with musical texture and expressive techniques, and analysing the features relevance and results uncovered interesting relations.
Automatic music classification and summarization
TLDR
This paper proposes effective algorithms to automatically classify and summarize music content and shows a better performance in music classification than traditional Euclidean distance methods and hidden Markov model methods.
Musical genre classification of audio signals
TLDR
The automatic classification of audio signals into an hierarchy of musical genres is explored and three feature sets for representing timbral texture, rhythmic content and pitch content are proposed.
Large-Scale MIDI-based Composer Classification
TLDR
This work is the first to investigate the composer classification problem with up to 100 composers using GiantMIDI-Piano, a transcription-based dataset and proposes to use piano rolls, onset rolls, and velocity rolls as input representations and use deep neural networks as classifiers.
...
...