Reinforcement Learning-Based Adaptive Electronic Synthesizer Piano Audio Denoising

This study proposes an adaptive electronic synthesizer piano audio noise reduction method based on reinforcement learning. By constructing and training a deep Q network (DQN), it achieves the ability to dynamically select optimal noise reduction parameters in a noisy environment. This significantly improves the quality of the audio signal. Experimental results show that the DQN-based method is significantly better than the traditional noise reduction method in terms of signal-to-noise ratio (SNR) and perceived sound quality (MOS), especially in high-noise frequency bands. The system architecture includes audio input module, reinforcement learning model, noise reduction processing module and audio output module. The experiment used multiple data sets and verified the effectiveness and robustness of the model through data preprocessing, model training, noise reduction processing and result evaluation steps. Research results show that this method has strong adaptability when dealing with complex audio environments and provides a new solution for high-quality audio processing.