| Title | A machine learning approach to violin timbre quality classification |
| Publication Type | Master Thesis |
| Year of Publication | 2018 |
| Authors | Lladó, P. |
| Abstract | Timbre definition has been traditionally unclear and it is difficult to find a precise
definition related to timbre attributes, since several factors contribute to its perception.
In this thesis, we present a machine learning approach to automatically classify
expert defined timbral attributes, by extracting audio features from violin recordings.
Features were extracted using the ESSENTIA library and machine learning
models were obtained to classify the aforementioned timbre attributes. Automatic
feature selection tools were used to study the most relevant features for classification.
Results might indicate that the extracted audio features contain sufficient
information to correctly classify the studied timbral attributes.
|
| Keywords | feature selection, Machine learning, mir, Timbre, Violin |
| Final publication | https://doi.org/10.5281/zenodo.1468983 |
