We present a deep neural network-based methodology for synthesising percussive sounds with control over high-level timbral characteristics of the sounds. This approach allows for intuitive control of a synthesizer, enabling the user to shape sounds without extensive knowledge of signal processing. We use a feedforward convolutional neural network-based architecture, which is able to map input parameters to the corresponding waveform. We propose two datasets to evaluate our approach on both a restrictive context, and in one covering a broader spectrum of sounds. The timbral features used as parameters are taken from recent literature in signal processing. We also use these features for evaluation and validation of the presented model, to ensure that changing the input parameters produces a congruent waveform with the desired characteristics. Finally, we evaluate the quality of the output sound using a subjective listening test. We provide sound examples and the system's source code for reproducibility.

中文翻译：

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由高级音色特征参数化的神经打击乐合成

我们提出了一种基于深度神经网络的方法，用于通过控制声音的高级音色特征来合成打击乐声音。这种方法允许对合成器进行直观控制，使用户能够在没有广泛的信号处理知识的情况下塑造声音。我们使用基于前馈卷积神经网络的架构，该架构能够将输入参数映射到相应的波形。我们提出了两个数据集来评估我们在限制性上下文中的方法，一个覆盖更广泛的声音。用作参数的音色特征取自信号处理领域的最新文献。我们还使用这些功能来评估和验证所呈现的模型，以确保更改输入参数会产生具有所需特征的一致波形。最后，我们使用主观听力测试来评估输出声音的质量。我们提供了合理的示例和系统的可重复性源代码。