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Music Recognition Using Blockchain Technology and Deep Learning.

Xize Chen¹, Xiaoyu Qu¹, Yufeng Qian²

¹School of Music and Dance, Qiqihar University, Qiqihar 161000, China.

Computational Intelligence and Neuroscience

|August 18, 2022

Summary

This summary is machine-generated.

This study introduces a deep learning music recognition method using hashing to improve classification accuracy. Blockchain technology is integrated to enhance digital music copyright protection in the big data era.

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Area of Science:

Computer Science
Music Information Retrieval
Blockchain Technology

Background:

The proliferation of digital music necessitates robust copyright protection systems.
Big data environments present unique challenges for music recognition and intellectual property management.

Purpose of the Study:

To develop a novel music recognition method using deep learning and hashing.
To enhance the copyright protection system for original digital music leveraging blockchain technology.

Main Methods:

A music recognition approach combining Convolutional Neural Network (CNN) with hashing learning was proposed.
The Convolutional Recurrent Neural Network Hashing (CRNNH) algorithm was developed, considering hash code semantic similarity.
A modular system for digital music property rights protection was designed using blockchain technology.

Main Results:

The CRNNH algorithm demonstrated superior music recognition performance across various bit numbers.
The blockchain-based system achieved a stable Query Per Second (QPS) of approximately 20,000 with 100 connections.
System pressure increased significantly with a higher number of analog connections.

Conclusions:

The proposed deep learning and hashing algorithm significantly improves music recognition classification accuracy.
Blockchain technology effectively safeguards digital music copyrights and ensures system operational performance.