Wireless hand gesture controlled multiple musical instruments

Jhamb, Pranshu; Rehalia, Arvind

doi:10.1007/s41870-018-0096-1

Cited by 4 publications

(1 citation statement)

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“…Classical music clips of a fixed length of 20 seconds were used in the system for training, and emotion categories were manually tagged for the music, and their system achieved 85% accuracy on their constructed dataset. Literature [6] uses parametric regression methods to predict the coordinates of music in the two-dimensional space of the valence-arousal sentiment model. Support vector regression was used to map the extracted acoustic features to the twodimensional space of the V-A model.…”

Section: Related Workmentioning

confidence: 99%

Wireless Play Music Beep Sensor to Assist in Music Fountain Control

Wang

2022

Journal of Sensors

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Musical water landscape combines landscape design with audiovisual art and is loved by the general public. However, the performance control form of musical water landscape generally adopts the manual offline preprogramming method, which needs to use the human subject’s consciousness to perceive the style, emotion, and other advanced features of the music first and then choreographs the corresponding landscape performance, which is a high-cost design form and low usage rate of the landscape and is not conducive to the expansion of the music library. In this paper, we design and implement a musical water landscape simulation system using OpenGL and 3Dmax technology in combination with wireless music buzzer sensors, extract and analyze the music features based on this system, and study the system control of musical water landscape. Open GL particle system technology is used to realize the dynamic simulation of weather and fountain inside the landscape and improve the drawing scheme from both modeling and rendering aspects. For the fountain, the physical model and the wireless music beep sensor are combined to design a variety of water type actions for a single spout, and the parameter interface to control the water type changes is reserved. We propose a style recognition method based on CRNN and residual network, which has a higher accuracy rate compared with the existing methods; we use the DP algorithm to segment the pitch sequence of the music to reduce the redundancy of information and improve the calculation speed of the system.

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Section: Related Workmentioning

confidence: 99%