Matthias Mauch
Biographic Data
| ID | 1753571 |
|---|---|
| NAME | Matthias Mauch |
| GIVEN NAMES | Matthias |
| FAMILY NAME | Mauch |
| SIGNATURE | MAUCH M |
| AFFILIATIONS | Queen Mary University of London |
| ORCID | 0000-0002-4352-6809 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2010 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 0 |
On revolutions
Sometimes the normal course of events is disrupted by a particularly swift and profound change. Historians have often referred to such changes as “revolutions”, and, though they have identified many of them, they have rarely supported their claims with statistical evidence. Here, we present a method to identify revolutions based on a measure of multivariate rate of change called Foote novelty. We define revolutions as those periods of time when t…
The pace of modern culture
Investigating style evolution of Western classical music
In musicology, there has been a long debate about a meaningful partitioning and description of music history regarding composition styles. Particularly, concepts of historical periods have been criticized since they cannot account for the continuous and interwoven evolution of style. To systematically study this evolution, large corpora are necessary suggesting the use of computational strategies. This article presents such strategies and experim…
TempEst
Issues concerning tuning and temperament bear relevance to music research in areas such as historical musicology, performance and recording studies, and music perception. We have recently demonstrated that it is possible to classify keyboard temperament automatically from audio recordings of standard musical works to the extent of accurately distinguishing between six different temperaments often used in harpsichord recordings. The current paper …
Improving Music Genre Classification Using Automatically Induced Harmony Rules
We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which fo…
No prominent works on this page.
TempEst
Issues concerning tuning and temperament bear relevance to music research in areas such as historical musicology, performance and recording studies, and music perception. We have recently demonstrated that it is possible to classify keyboard temperament automatically from audio recordings of standard musical works to the extent of accurately distinguishing between six different temperaments often used in harpsichord recordings. The current paper …
Improving Music Genre Classification Using Automatically Induced Harmony Rules
We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which fo…
Investigating style evolution of Western classical music
In musicology, there has been a long debate about a meaningful partitioning and description of music history regarding composition styles. Particularly, concepts of historical periods have been criticized since they cannot account for the continuous and interwoven evolution of style. To systematically study this evolution, large corpora are necessary suggesting the use of computational strategies. This article presents such strategies and experim…
On revolutions
Sometimes the normal course of events is disrupted by a particularly swift and profound change. Historians have often referred to such changes as “revolutions”, and, though they have identified many of them, they have rarely supported their claims with statistical evidence. Here, we present a method to identify revolutions based on a measure of multivariate rate of change called Foote novelty. We define revolutions as those periods of time when t…
The pace of modern culture
Computer Science (5 works) · Art (3 works) · Artificial Intelligence (3 works) · Music and Audio Processing (3 works) · Music Technology and Sound Studies (3 works) · Psychology (3 works) · Epistemology (2 works) · Philosophy (2 works) · Aesthetics (1 work) · Anthropology (1 work)