摘要
随着广播电视技术的快速发展,音频质量对节目制作和传播的重要性日益凸显。然而,各种噪声干扰严重影响音频信号清晰度。深度学习技术为音频降噪提供了新的解决方案。通过构建深度神经网络模型,利用大规模数据训练,可以有效识别和去除复杂噪声,显著提升音频质量。实验结果表明,相较于传统算法,基于深度学习的降噪方法具有更优的性能,在保持音频本体信息的同时,能够有效抑制多种类型的噪声干扰,为广播电视音频处理提供了技术支撑。
With the rapid development of radio and television technology,the importance of audio quality to program production and dissemination has become increasingly prominent.However,all kinds of noise interference seriously affect the clarity of audio signals.Deep learning technology provides a new solution for audio noise reduction.By constructing a deep neural network model and using large-scale data training,complex noise can be effectively identified and removed,and audio quality can be significantly improved.The experimental results show that,compared with the traditional algorithm,the denoising method based on deep learning has better performance,and can effectively suppress various types of noise interference while maintaining the audio ontology information,which provides technical support for radio and television audio processing.
作者
李志远
LI Zhiyuan(Shouguang Convergence Media Center,Shouguang 262700,China)
出处
《电声技术》
2024年第12期122-125,共4页
Audio Engineering
关键词
深度学习
广播电视
音频降噪
神经网络
信号处理
deep learning
radio and television
audio denoising
neural networks
signal processing
