Darrell Conklin
Biographic Data
| ID | 3820531 |
|---|---|
| NAME | Darrell Conklin |
| GIVEN NAMES | Darrell |
| FAMILY NAME | Conklin |
| SIGNATURE | CONKLIN D |
| AFFILIATIONS | University of the Basque Country |
| ORCID | 0000-0002-2313-9326 |
| VERIFIED | No |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 19 |
| AUTHOR COUNT | 8 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1995 |
| LATEST PUBLICATION YEAR | 2018 |
| H-INDEX | 2 |
Supervised descriptive pattern discovery in Native American music
The discovery of recurrent patterns in groups of songs is an important first step in computational corpus analysis. In this paper, computational techniques of supervised descriptive pattern discovery are applied to model and extend ethnomusicological analyses of Native American music. Using a corpus of over 2000 songs collected and transcribed by anthropologist Frances Densmore and building on Densmore’s own music content features, the analysis i…
Music Generation from Statistical Models of Harmony
This article investigates the problem of sampling from statistical models of music, motivated by the fact that music generated by random walk is generally atypical in style and of vastly inferior quality compared with pieces in the corpus. Specifically, we employ a multiple viewpoint system of four-part harmony, in conjunction with a small set of general rules of harmony, to evaluate an improved iterative random walk technique which efficiently f…
New Directions in Music and Machine Learning
Further special thanks are given to Alan Marsden, who enthusiastically supported this Special Issue since its inception
Antipattern Discovery in Folk Tunes
This paper presents a new pattern discovery method for labelled folk song corpora. The method discovers general patterns that are rare or even entirely absent from a set of pieces, and among those the patterns that are frequent in a background set. Pattern discovery is performed with reference to a background ontology of folk tune genres. The method is applied to a large corpus of Basque folk tunes and results are evaluated as descriptive pattern…
Multiple Viewpoint Systems for Music Classification
This paper describes a new statistical modelling method for music classification. The method is an extension of the multiple viewpoint method for music prediction and generation. A multiple viewpoint system significantly outperforms all component viewpoints on the tasks of folk tune genre and region classification. The method is successfully applied to predict the genres of unlabelled Basque folk tunes
Introduction to the Special Issue on Music and Machine Learning
Comparative Pattern Analysis of Cretan Folk Songs
This paper reports on data mining of Cretan folk songs for distinctive patterns. A pattern is distinctive if it occurs with higher probability in a corpus as compared to an anticorpus. A small set of transcribed Cretan folk songs was encoded, organized using a knowledge base of classes, and mined using distinctive pattern discovery methods. In this exploratory study several highly distinctive melodic patterns emerge, indicating the ability of dis…
Multiple viewpoint systems for music prediction
This paper examines the prediction and generation of music using a multiple viewpoint system, a collection of independent views of the musical surface each of which models a specific type of musical phenomena. Both the general style and a particular piece are modeled using dual short‐term and long‐term theories, and the model is created using machine learning techniques on a corpus of musical examples. The models are used for analysis and predict…
Multiple viewpoint systems for music prediction
This paper examines the prediction and generation of music using a multiple viewpoint system, a collection of independent views of the musical surface each of which models a specific type of musical phenomena. Both the general style and a particular piece are modeled using dual short‐term and long‐term theories, and the model is created using machine learning techniques on a corpus of musical examples. The models are used for analysis and predict…
Multiple Viewpoint Systems for Music Classification
This paper describes a new statistical modelling method for music classification. The method is an extension of the multiple viewpoint method for music prediction and generation. A multiple viewpoint system significantly outperforms all component viewpoints on the tasks of folk tune genre and region classification. The method is successfully applied to predict the genres of unlabelled Basque folk tunes
Comparative Pattern Analysis of Cretan Folk Songs
This paper reports on data mining of Cretan folk songs for distinctive patterns. A pattern is distinctive if it occurs with higher probability in a corpus as compared to an anticorpus. A small set of transcribed Cretan folk songs was encoded, organized using a knowledge base of classes, and mined using distinctive pattern discovery methods. In this exploratory study several highly distinctive melodic patterns emerge, indicating the ability of dis…
Music Generation from Statistical Models of Harmony
This article investigates the problem of sampling from statistical models of music, motivated by the fact that music generated by random walk is generally atypical in style and of vastly inferior quality compared with pieces in the corpus. Specifically, we employ a multiple viewpoint system of four-part harmony, in conjunction with a small set of general rules of harmony, to evaluate an improved iterative random walk technique which efficiently f…
Multiple viewpoint systems for music prediction
This paper examines the prediction and generation of music using a multiple viewpoint system, a collection of independent views of the musical surface each of which models a specific type of musical phenomena. Both the general style and a particular piece are modeled using dual short‐term and long‐term theories, and the model is created using machine learning techniques on a corpus of musical examples. The models are used for analysis and predict…
Introduction to the Special Issue on Music and Machine Learning
Comparative Pattern Analysis of Cretan Folk Songs
This paper reports on data mining of Cretan folk songs for distinctive patterns. A pattern is distinctive if it occurs with higher probability in a corpus as compared to an anticorpus. A small set of transcribed Cretan folk songs was encoded, organized using a knowledge base of classes, and mined using distinctive pattern discovery methods. In this exploratory study several highly distinctive melodic patterns emerge, indicating the ability of dis…
Antipattern Discovery in Folk Tunes
This paper presents a new pattern discovery method for labelled folk song corpora. The method discovers general patterns that are rare or even entirely absent from a set of pieces, and among those the patterns that are frequent in a background set. Pattern discovery is performed with reference to a background ontology of folk tune genres. The method is applied to a large corpus of Basque folk tunes and results are evaluated as descriptive pattern…
Multiple Viewpoint Systems for Music Classification
This paper describes a new statistical modelling method for music classification. The method is an extension of the multiple viewpoint method for music prediction and generation. A multiple viewpoint system significantly outperforms all component viewpoints on the tasks of folk tune genre and region classification. The method is successfully applied to predict the genres of unlabelled Basque folk tunes
New Directions in Music and Machine Learning
Further special thanks are given to Alan Marsden, who enthusiastically supported this Special Issue since its inception
Music Generation from Statistical Models of Harmony
This article investigates the problem of sampling from statistical models of music, motivated by the fact that music generated by random walk is generally atypical in style and of vastly inferior quality compared with pieces in the corpus. Specifically, we employ a multiple viewpoint system of four-part harmony, in conjunction with a small set of general rules of harmony, to evaluate an improved iterative random walk technique which efficiently f…
Supervised descriptive pattern discovery in Native American music
The discovery of recurrent patterns in groups of songs is an important first step in computational corpus analysis. In this paper, computational techniques of supervised descriptive pattern discovery are applied to model and extend ethnomusicological analyses of Native American music. Using a corpus of over 2000 songs collected and transcribed by anthropologist Frances Densmore and building on Densmore’s own music content features, the analysis i…
Computer Science (8 works) · Music and Audio Processing (8 works) · Music Technology and Sound Studies (7 works) · Artificial Intelligence (6 works) · Art (5 works) · Musical (4 works) · Natural language processing (4 works) · Machine learning (3 works) · Folk music (2 works) · History (2 works)