Improving Drumming Robot Via Attention Transformer Network

TL;DR

This paper presents an improved drumming robot that utilizes an attention transformer network to enhance music transcription and drum classification, enabling smarter entertainment applications.

Contribution

Introduction of an attention transformer network-based method for automatic music transcription in drumming robots, improving their classification accuracy and application capabilities.

Findings

Enhanced drum classification performance

Efficient handling of sequential audio data

Potential for smarter entertainment applications

Abstract

Robotic technology has been widely used in nowadays society, which has made great progress in various fields such as agriculture, manufacturing and entertainment. In this paper, we focus on the topic of drumming robots in entertainment. To this end, we introduce an improving drumming robot that can automatically complete music transcription based on the popular vision transformer network based on the attention mechanism. Equipped with the attention transformer network, our method can efficiently handle the sequential audio embedding input and model their global long-range dependencies. Massive experimental results demonstrate that the improving algorithm can help the drumming robot promote drum classification performance, which can also help the robot to enjoy a variety of smart applications and services.