Spectral Rolloff Images for Multi-class Human Action Analysis : A Benchmark Dataset

This dataset contains a comprehensive collection of spectral rolloff values representing a variety of human actions. Spectral rolloff is a critical feature in digital signal processing that signifies the frequency below which a specified percentage of the total spectral energy resides. The values encapsulated in this dataset correspond to diverse human actions such as walking, running, jumping, and dancing. The spectral rolloff values are derived by analyzing the power spectrum of the audio signals associated with each action. These values provide a measure of the frequency content of the audio signal, offering insights into the nature of the corresponding action. Each spectral rolloff representation corresponds to a segment of the audio signal. The dataset has been purposefully curated for tasks including human action recognition, classification, segmentation, and detection. It provides an essential tool for the training and evaluation of machine learning models focused on interpreting human actions based on audio signals. Researchers and practitioners in the fields of signal processing, computer vision, and machine learning can find the dataset particularly beneficial, especially those interested in crafting algorithms for human action analysis leveraging audio signals. Importantly, the dataset includes annotations with labels that indicate the type of human action represented by each spectral rolloff. This labeled information promotes a supervised learning environment, vital for the development and assessment of predictive models.本数据集收录了涵盖多种人类动作的频谱滚降（spectral rolloff）值集合。频谱滚降是数字信号处理领域的关键特征，指总频谱能量中特定占比所对应的截止频率，即低于该频率的分量包含了总频谱能量的指定比例。本数据集收录的滚降值对应行走、奔跑、跳跃、舞蹈等多样化人类动作。这些频谱滚降值通过分析各动作对应音频信号的功率谱计算得到，可表征音频信号的频率分布特征，助力解析对应人类动作的本质属性。每一组频谱滚降表征结果均对应一段音频信号片段。本数据集专为人类动作识别、分类、分割与检测等任务精心构建，为基于音频信号解析人类动作的机器学习模型的训练与评估提供了核心工具。信号处理、计算机视觉与机器学习领域的研究人员与从业者可从本数据集获益良多，尤其是那些致力于基于音频信号开发人类动作分析算法的从业者。尤为重要的是，本数据集包含标注信息，其标签可指明每组频谱滚降结果所对应的人类动作类别。这类带标注的信息可构建监督学习场景，对预测模型的开发与评估至关重要。