Timbre Audio Dataset (DX7-clone synthesizer)

22.5 hours of synthesized audio using the open-source learnfm clone of the DX7 FM synthesizer, based upon 31K presets from Bobby Blue. These represent ``natural'' synthesis sounds---i.e.presets devised by humans. We generated 4-second samples playing midi note 69 (A440) with a note-on duration of 3 seconds. For each preset, we varied only the velocity, from 1--127, and perceptually normalized the level of each sound. Sounds that were completely identical were removed from the dataset. DX7 FM synthesis is good for this purpose because it doesn't have a noise oscillator. Thus, for a particular preset, there is a timbral variation as the velocity increases. 8K presets had only one unique sound. The median was 51 unique sound per preset, mean 41.9, stddev 27.4. We used the Surge Python API to generate this dataset. Applications of this dataset include: Timbre ranking within a preset Predict a sound's preset本数据集包含22.5小时的合成音频，采用基于Bobby Blue提供的31000组预设参数的DX7调频（FM）合成器开源复刻项目learnfm生成。这些音频均为“自然”合成音色——即由人类设计的预设参数对应的音色。我们生成了时长4秒的音频片段，演奏MIDI（乐器数字接口）音符69（标准音A440），音符触发时长为3秒。针对每一组预设参数，我们仅改变力度值（取值范围1至127），并对每段音频的电平进行感知归一化处理。完全相同的音频片段已从数据集中移除。选用DX7 FM合成器开展此类任务的优势在于其未配备噪声振荡器，因此针对某一特定预设参数，随着力度提升，音色会产生可感知的变化。其中8000组预设参数仅对应一种独特音色。数据集的统计结果为：每个预设参数对应的独特音色数量的中位数为51，均值为41.9，标准差为27.4。本数据集通过Surge Python API生成。该数据集的应用场景包括：预设参数内的音色排名、预测声音所属的预设参数。