TUT-SED Synthetic 2016 Dataset

TUT-SED Synthetic 2016 contains of mixture signals artificially generated from isolated sound events samples. This approach is used to get more accurate onset and offset annotations than in dataset using recordings from real acoustic environments where the annotations are always subjective.
Mixture signals in the dataset are created by randomly selecting and mixing isolated sound events from 16 sound event classes together. The resulting mixtures contains sound events with varying polyphony. All together 994 sound event samples were purchased from Sound Ideas. From the 100 mixtures created, 60% were assigned for training, 20% for testing and 20% for validation. The total amount of audio material in the dataset is 566 minutes.
Different instances of the sound events are used to synthesize the training, validation and test partitions. Mixtures were created by randomly selecting event instance and from it, randomly, a segment of length 3-15 seconds. Between events, random length silent region was introduced. Such tracks were created for four to nine event classes, and were then mixed together to form the mixture signal. As sound events are not consistently active during the samples (e.g. footsteps), automatic signal energy based annotation was applied to obtain accurate event activity within the sample. Annotation of the mixture signal was created by pooling together event activity annotation of used samples.

TUT-SED 综合合成 2016 数据集由人工合成的混合信号构成，这些信号源自独立的声事件样本。该方法的运用旨在获得比使用真实声学环境录音的数据集更精确的起始和终止标注，因为在真实声学环境中，标注始终具有主观性。
数据集中的混合信号通过随机选择和混合来自 16 个声事件类的独立声事件而创建。这些混合信号包含具有不同和声的声事件。总计从 Sound Ideas 购买了 994 个声事件样本。从创建的 100 个混合中，60% 被分配用于训练，20% 用于测试，20% 用于验证。数据集中音频材料总量为 566 分钟。
训练、验证和测试分区采用了不同的声事件实例进行合成。混合信号的创建是通过随机选择事件实例，并从中随机选取长度为 3-15 秒的片段。事件之间引入了随机长度的静默区域。为四个至九个事件类创建了此类轨道，然后将它们混合在一起形成混合信号。由于声事件在样本中并非始终活跃（例如，脚步声），因此应用了基于信号能量的自动标注，以获得样本中声事件的准确活动信息。混合信号的标注是通过汇总所使用样本的事件活动标注而创建的。