使用扩张残差网络的自适应共振均衡
Auto-adaptive Resonance Equalization using Dilated Residual Networks
AI总结：
针对音乐和音频制作中频谱共振衰减问题，提出双组件系统，用动态均衡器检测共振，深度神经网络预测最佳衰减因子，测试两种网络架构，其预测效果优于基线方法。
摘要：
在音乐和音频制作中，频谱共振的衰减是获得技术上正确结果的重要一步。本文提出了一个双组件系统来自动执行共振均衡任务。第一个组件是动态均衡器，可自动检测共振并按用户指定的因子进行衰减。第二个组件是深度神经网络，它基于加窗音频预测最佳衰减因子。该网络在从实验收集的经验数据上进行训练和验证，在该实验中，声音工程师为一组音轨选择他们喜欢的衰减因子。我们测试了预测模型的两种不同网络架构，发现直接对音频信号操作的扩张残差网络与需要先进行音频特征提取阶段的网络架构相当。两种架构预测人类偏好的共振衰减因子都明显优于基线方法。
英文摘要：
In music and audio production, attenuation of spectral resonances is an important step towards a technically correct result. In this paper we present a two-component system to automate the task of resonance equalization. The first component is a dynamic equalizer that automatically detects resonances and offers to attenuate them by a user-specified factor. The second component is a deep neural network that predicts the optimal attenuation factor based on the windowed audio. The network is trained and validated on empirical data gathered from an experiment in which sound engineers choose their preferred attenuation factors for a set of tracks. We test two distinct network architectures for the predictive model and find that a dilated residual network operating directly on the audio signal is on a par with a network architecture that requires a prior audio feature extraction stage. Both architectures predict human-preferred resonance attenuation factors significantly better than a baseline approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
