A Survey on Visualizations for Musical Data

Abstract

Digital methods are increasingly applied to store, structure and analyse vast amounts of musical data. In this context, visualization plays a crucial role, as it assists musicologists and non‐expert users in data analysis and in gaining new knowledge. This survey focuses on this unique link between musicology and visualization. We classify 129 related works according to the visualized data types, and we analyse which visualization techniques were applied for certain research inquiries and to fulfill specific tasks. Next to scientific references, we take commercial music software and public websites into account, that contribute novel concepts of visualizing musicological data. We encounter different aspects of uncertainty as major problems when dealing with musicological data and show how occurring inconsistencies are processed and visually communicated. Drawing from our overview in the field, we identify open challenges for research on the interface of musicology and visualization to be tackled in the future.

Digital Music Waveform Analysis and Retrieval Based on Feature Extraction Algorithm

“…Literature [14] further proposes an algorithm for extracting music abstracts and chorus segments (chorus) using the results of structural analysis by analyzing the hierarchical structure of music signals. Literature [15] studies music abstracts from the self-similarity analysis of music signals. Literature [16] proposed music clustering based on similarity analysis of music content and tried to apply it to the automatic generation of playlists of music playback software.…”

Detecting Text Reuse and Similarities between Artists in Rap Music through Visualization

“…Similarity Analysis of Musicians is one of the applied use cases in the STAR about Visualizations for Musical Data by Khulusi et al [8]. Although the text of a song is not directly musical data it is still connected to the music and the musician.…”

Uncertainty-aware Visual Analytics - Scope, Oppertunities and Challenges

“…Here, a variety of sources of uncertainty can be contained in the data ranging from ambiguity over missing values to uncertain sources of data. The computational analysis of data originating from the digital humanities community has become very popular in recent years, where uncertainty inherent in the data has been named as one major challenge [55].…”