Ballroom and Artificial Intelligence
Ballroom dance step type recognition by random forest using video and wearable sensor
The paper presents a hybrid ballroom dance step type recognition method using video and wearable sensors. Learning ballroom dance is very difficult for less experienced dancers as it has many complex types of steps. Therefore, our purpose is to recognize the various step types to support step learning. While the major approach to recognize dance performance is to utilize video, we cannot simply adopt it for ballroom dance because the dancers' images overlap each other. To solve the problem, we propose a hybrid step recognition method combining video and wearable sensors for enhancing its accuracy and robustness. We collect seven dancers' video and wearable sensors data including acceleration, angular velocity, and body parts location change. After that, we pre-process them and extract some feature values to recognize the step types. By adopting Random Forest for recognition, we confirmed that our approach achieved fl-score 0.760 for 13 step types recognition. Finally, we will open our dataset of ballroom dance to HASCA community for further research opportunities.
Tango or Waltz?: Putting Ballroom Dance Style into Tempo Detection
Rhythmic information plays an important role in Music Information Retrieval. Example applications include automatically annotating large databases by genre, meter, ballroom dance style or tempo, fully automated D.J.-ing, and audio segmentation for further retrieval tasks such as automatic chord labeling. In this article, we therefore provide an introductory overview over basic and current principles of tempo detection. Subsequently, we show how to improve on these by inclusion of ballroom dance style recognition. We introduce a feature set of 82 rhythmic features for rhythm analysis on real audio. With this set, data-driven identification of the meter and ballroom dance style, employing support vector machines, is carried out in a first step. Next, this information is used to more robustly detect tempo. We evaluate the suggested method on a large public database containing 1.8 k titles of standard and Latin ballroom dance music. Following extensive test runs, a clear boost in performance can be reported.