Abstract
Assessing the relationship between chord sequences is an important ongoing research topic in the fields of music cognition and music information retrieval. Heuristic and cognitive models of chord similarity have been investigated but none has aimed to capture the collective perception of chord similarity from a large dataset of user-generated content.
Devising a large-scale experiment to gather sufficient data from human subjects has always been a major stumbling block. We present a novel chord similarity model based on
a large amount of crowd-sourced transcriptions from a popular automatic chord estimation service. We show that our model outperforms heuristic-based models in a song identification task.
Secondly, a model of chord mutations based on a large amount of crowd-sourced cover songs transcriptions is introduced. From crowd-sourced data, we create substitution matrices that capture the perceived similarity and mutability between chords.
These results show that modelling the collective perception can not only substitute alternative, sophisticated models but also further enhance performance in various music information retrieval tasks.
Devising a large-scale experiment to gather sufficient data from human subjects has always been a major stumbling block. We present a novel chord similarity model based on
a large amount of crowd-sourced transcriptions from a popular automatic chord estimation service. We show that our model outperforms heuristic-based models in a song identification task.
Secondly, a model of chord mutations based on a large amount of crowd-sourced cover songs transcriptions is introduced. From crowd-sourced data, we create substitution matrices that capture the perceived similarity and mutability between chords.
These results show that modelling the collective perception can not only substitute alternative, sophisticated models but also further enhance performance in various music information retrieval tasks.
| Original language | English |
|---|---|
| Title of host publication | The Second IEEE International Conference on Multimedia Big Data |
| Publication status | Published - 2016 |