Mining Data for Music Recognition in Big Data Using Locality-Sensitive Hashing (LSH) Algorithm

In the ever-evolving landscape of the music industry, the ability to efficiently recognize and categorize vast amounts of musical data is paramount. This study explores the application of data mining techniques for music recognition within the realm of Big Data, leveraging the Locality-Sensitive Hashing (LSH) algorithm. As the volume of digital music continues to grow exponentially, traditional methods of music recognition and classification face significant challenges in terms of scalability and efficiency. LSH, known for its ability to approximate nearest neighbor searches in high-dimensional spaces, offers a promising solution to these challenges.By implementing LSH, this research aims to improve the speed and accuracy of music recognition processes. The study delved into the mechanics of LSH, explaining how it hashes similar music data points into the same buckets with high probability, thereby facilitating quick and efficient retrieval. Through extensive experimentation and analysis, the effectiveness of LSH in handling large-scale music datasets is evaluated, highlighting its advantages over conventional methods. The findings of this study underscore the potential of LSH to revolutionize music recognition in Big Data environments, offering a scalable, efficient, and robust approach to managing and categorizing extensive musical archives. This research not only contributes to the academic discourse on data mining and music recognition but also provides practical insights for industry practitioners seeking to harness the power of Big Data in the music domain.