Intelligent Analysis and Classification of Piano Music Gestures with Multimodal Recordings

In the traditional recording system, recording any music includes a sizeable instrumental setup and allocates space for the music players. Lighter and fewer devices are replacing larger instruments due to technological advancement and epidemic environmental conditions. This research focuses on text, but audio and video types are also considered. Multiple signal classification with a 5G-based wireless communication network algorithm is implemented to perform the automatic recording and classification of the music data. In this research, a multi-modal gesture recognition dataset is considered for analysis. The dataset was obtained using sensor networks and an intelligent system to record the musical gestures and classify the recorded gestures. The development of machine learning algorithms is not limited to similar technological concepts. Still, it extends to almost all other technical resources such as the 5G network, signal processing, networking, and all other technical resources. This would lead to additional engineering challenges that are utilized in most cases, such as the development of gestures with multi-mode recording. This research has proposed MSA with WCN algorithm to perform intelligent analysis and classification of piano music gestures and is compared with the existing K-Means algorithm and achieved an accuracy of 99.12%.