Improving Drumming Robot Via Attention Transformer Network
This work addresses a domain-specific problem in entertainment robotics, with incremental improvements to existing methods.
The paper tackles the problem of music transcription for drumming robots by introducing an attention transformer network to process sequential audio embeddings, resulting in improved drum classification performance.
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.