Application of LSTM Feature Extraction Model in the Construction of Opera Audio Database

Abstract

Chinese traditional music plays a greater role in ideal society governed by rites and rituals. The musical culture of China is both ancient and modern. Throughout history, musicians have created several musical genres and used countless instruments. In order to appreciate Chinese traditional music, it is helpful to have a basic familiarity with three "keynotes": the classical traditional music of the elites; folk and ethnic music, and the most popular traditional Chinese instruments. In general, an opera audio database consists of audio recordings of opera performances. These recordings may be utilized for a variety of reasons, including research, education, and entertainment. To establish such a database, pertinent features must be extracted from the audio recordings to allow for effective storage, retrieval, and analysis of the data. Improvements in audio signal processing over the last several years have qualified for creating feature extraction methods that improve the quality and accuracy of the retrieved features. We first gather the audio recordings of opera performances using an appropriate recording device to build an opera audio database. Data preprocessing then eliminates noise and other undesired signals from the audio recordings to enhance the data quality. After that, to extract useful features from the audio recordings that may be utilized to define the sound's rates, the Long Short Term Memory (LSTM) technique is used. The components must then be organized and stored in a database for fast retrieval and analysis once extracted. A suitable database management system may be used to build the database. The database should be created to make it simple to search and filter the data using different criteria. The study results stated that building an opera audio database using novel feature extraction approaches is a challenging process that requires an understanding of opera music, database administration, and audio signal processing. The experimental results stated that the proposed model has provided an accuracy of 96%.