Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space
Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space
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DOI：
10.3390/app6040116
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发表时间：
2016-04
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影响因子：
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通讯作者：
Spyridon Stasis;R. Stables;Jason Hockman
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文献类型：
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作者：
Spyridon Stasis;R. Stables;Jason Hockman
均衡是声音制作中最常用的工具之一，允许用户控制音频信号中不同频率分量的增益。本文提出了一种将一组均衡参数映射到降维空间的模型。该方法的目的是允许用户通过减少参数数量和消除创造性地均衡输入音频所需的技术知识，以直观的方式与系统交互。所提出的模型代表了二维平面上的13个均衡器参数，该模型是用从语义均衡器插件提取的数据来训练的，使用音色形容词温暖和明亮。我们还包括一个参数加权阶段，以便将输入参数缩放到音频信号的频谱特征，使系统具有自适应能力。为了最大化模型的有效性，我们评估了各种降维和回归技术，评估了参数重建和结构保留在低维空间中的性能。在根据评价标准选择了合适的模型后，我们通过听力测试对系统进行了主观评价。
Equalisation is one of the most commonly-used tools in sound production, allowing users to control the gains of different frequency components in an audio signal. In this paper we present a model for mapping a set of equalisation parameters to a reduced dimensionality space. The purpose of this approach is to allow a user to interact with the system in an intuitive way through both the reduction of the number of parameters and the elimination of technical knowledge required to creatively equalise the input audio. The proposed model represents 13 equaliser parameters on a two-dimensional plane, which is trained with data extracted from a semantic equalisation plug-in, using the timbral adjectives warm and bright. We also include a parameter weighting stage in order to scale the input parameters to spectral features of the audio signal, making the system adaptive. To maximise the efficacy of the model, we evaluate a variety of dimensionality reduction and regression techniques, assessing the performance of both parameter reconstruction and structural preservation in low-dimensional space. After selecting an appropriate model based on the evaluation criteria, we conclude by subjectively evaluating the system using listening tests.