AIoT-Based Drum Transcription Robot using Convolutional Neural Networks

TL;DR

This paper presents an AIoT-based drum transcription robot utilizing a lightweight CNN model for real-time music transcription, integrating cloud, edge, and data nodes for efficient processing and deployment.

Contribution

It introduces a novel AIoT system with a lightweight CNN for real-time drum transcription, enhancing deployment efficiency and system performance.

Findings

Achieves competitive transcription accuracy.

Enables real-time processing on terminal devices.

Supports smart applications and services.

Abstract

With the development of information technology, robot technology has made great progress in various fields. These new technologies enable robots to be used in industry, agriculture, education and other aspects. In this paper, we propose a drum robot that can automatically complete music transcription in real-time, which is based on AIoT and fog computing technology. Specifically, this drum robot system consists of a cloud node for data storage, edge nodes for real-time computing, and data-oriented execution application nodes. In order to analyze drumming music and realize drum transcription, we further propose a light-weight convolutional neural network model to classify drums, which can be more effectively deployed in terminal devices for fast edge calculations. The experimental results show that the proposed system can achieve more competitive performance and enjoy a variety of smart applications and services.