Qingshan Yao

Title: Innovations of Qingshan Yao in Music Processing

Introduction

Qingshan Yao is a prominent inventor based in Shenzhen, China. He has made significant contributions to the field of music processing through his innovative patents. With a total of four patents to his name, Yao is recognized for his advancements in audio technology.

Latest Patents

One of Qingshan Yao's latest patents is a method and device for transparent processing of music. This invention involves obtaining characteristics of music to be played and inputting these characteristics into a transparency probability neural network. The network then determines a transparency enhancement parameter that is used to adjust the music accordingly. This method utilizes deep learning to create a mapping relationship between transparency probability and enhancement parameters, allowing for automatic transparency adjustments in music playback.

Another notable patent is his deep learning-based audio equalization method. This invention includes a tonal balancing method, apparatus, and system that extracts features from audio data to generate audio balancing results. By employing deep neural networks and unsupervised deep learning, Yao addresses the challenges of audio balancing for unlabeled music and music of unknown style. This invention also incorporates user preferences to achieve a more tailored audio balancing design.

Career Highlights

Qingshan Yao is currently associated with Anker Innovations Technology Co. Ltd., where he continues to develop cutting-edge audio technologies. His work has positioned him as a key figure in the field of music processing, contributing to advancements that enhance user experiences.

Collaborations

Yao collaborates with talented individuals such as Yu Qin and Haowen Yu, who contribute to his innovative projects. Their teamwork fosters an environment of creativity and technological advancement.

Conclusion

Qingshan Yao's contributions to music processing through his innovative patents demonstrate his expertise and commitment to advancing audio technology. His work not only enhances the quality of music playback but also showcases the potential of deep learning in audio applications.