A Dataset and Baselines for Measuring and Predicting the Music Piece Memorability
- URL: http://arxiv.org/abs/2405.12847v1
- Date: Tue, 21 May 2024 14:57:04 GMT
- Title: A Dataset and Baselines for Measuring and Predicting the Music Piece Memorability
- Authors: Li-Yang Tseng, Tzu-Ling Lin, Hong-Han Shuai, Jen-Wei Huang, Wen-Whei Chang,
- Abstract summary: We focus on measuring and predicting music memorability.
We train baselines to predict and analyze music memorability.
We demonstrate that while there is room for improvement, predicting music memorability with limited data is possible.
- Score: 16.18336216092687
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Nowadays, humans are constantly exposed to music, whether through voluntary streaming services or incidental encounters during commercial breaks. Despite the abundance of music, certain pieces remain more memorable and often gain greater popularity. Inspired by this phenomenon, we focus on measuring and predicting music memorability. To achieve this, we collect a new music piece dataset with reliable memorability labels using a novel interactive experimental procedure. We then train baselines to predict and analyze music memorability, leveraging both interpretable features and audio mel-spectrograms as inputs. To the best of our knowledge, we are the first to explore music memorability using data-driven deep learning-based methods. Through a series of experiments and ablation studies, we demonstrate that while there is room for improvement, predicting music memorability with limited data is possible. Certain intrinsic elements, such as higher valence, arousal, and faster tempo, contribute to memorable music. As prediction techniques continue to evolve, real-life applications like music recommendation systems and music style transfer will undoubtedly benefit from this new area of research.
Related papers
- A Survey of Foundation Models for Music Understanding [60.83532699497597]
This work is one of the early reviews of the intersection of AI techniques and music understanding.
We investigated, analyzed, and tested recent large-scale music foundation models in respect of their music comprehension abilities.
arXiv Detail & Related papers (2024-09-15T03:34:14Z) - MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Models [57.47799823804519]
We are inspired by how musicians compose music not just from a movie script, but also through visualizations.
We propose MeLFusion, a model that can effectively use cues from a textual description and the corresponding image to synthesize music.
Our exhaustive experimental evaluation suggests that adding visual information to the music synthesis pipeline significantly improves the quality of generated music.
arXiv Detail & Related papers (2024-06-07T06:38:59Z) - Fairness Through Domain Awareness: Mitigating Popularity Bias For Music
Discovery [56.77435520571752]
We explore the intrinsic relationship between music discovery and popularity bias.
We propose a domain-aware, individual fairness-based approach which addresses popularity bias in graph neural network (GNNs) based recommender systems.
Our approach uses individual fairness to reflect a ground truth listening experience, i.e., if two songs sound similar, this similarity should be reflected in their representations.
arXiv Detail & Related papers (2023-08-28T14:12:25Z) - A Dataset for Greek Traditional and Folk Music: Lyra [69.07390994897443]
This paper presents a dataset for Greek Traditional and Folk music that includes 1570 pieces, summing in around 80 hours of data.
The dataset incorporates YouTube timestamped links for retrieving audio and video, along with rich metadata information with regards to instrumentation, geography and genre.
arXiv Detail & Related papers (2022-11-21T14:15:43Z) - Novelty and Cultural Evolution in Modern Popular Music [0.0]
We compare musical artifacts to their contemporaries to identify novel artifacts.
Using Music Information Retrieval (MIR) data and lyrics from Billboard Hot 100 songs between 1974-2013, we calculate a novelty score for each song's aural attributes and lyrics.
arXiv Detail & Related papers (2022-06-15T18:25:39Z) - Multi-task Learning with Metadata for Music Mood Classification [0.0]
Mood recognition is an important problem in music informatics and has key applications in music discovery and recommendation.
We propose a multi-task learning approach in which a shared model is simultaneously trained for mood and metadata prediction tasks.
Applying our technique on the existing state-of-the-art convolutional neural networks for mood classification improves their performances consistently.
arXiv Detail & Related papers (2021-10-10T11:36:34Z) - MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training [97.91071692716406]
Symbolic music understanding refers to the understanding of music from the symbolic data.
MusicBERT is a large-scale pre-trained model for music understanding.
arXiv Detail & Related papers (2021-06-10T10:13:05Z) - Musical Prosody-Driven Emotion Classification: Interpreting Vocalists
Portrayal of Emotions Through Machine Learning [0.0]
The role of musical prosody remains under-explored despite several studies demonstrating a strong connection between prosody and emotion.
In this study, we restrict the input of traditional machine learning algorithms to the features of musical prosody.
We utilize a methodology for individual data collection from vocalists, and personal ground truth labeling by the artist themselves.
arXiv Detail & Related papers (2021-06-04T15:40:19Z) - Music Embedding: A Tool for Incorporating Music Theory into
Computational Music Applications [0.3553493344868413]
It is important to digitally represent music in a music theoretic and concise manner.
Existing approaches for representing music are ineffective in terms of utilizing music theory.
arXiv Detail & Related papers (2021-04-24T04:32:45Z) - Multi-Modal Music Information Retrieval: Augmenting Audio-Analysis with
Visual Computing for Improved Music Video Analysis [91.3755431537592]
This thesis combines audio-analysis with computer vision to approach Music Information Retrieval (MIR) tasks from a multi-modal perspective.
The main hypothesis of this work is based on the observation that certain expressive categories such as genre or theme can be recognized on the basis of the visual content alone.
The experiments are conducted for three MIR tasks Artist Identification, Music Genre Classification and Cross-Genre Classification.
arXiv Detail & Related papers (2020-02-01T17:57:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.