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

Published: 25 July 2023| Version 1 | DOI: 10.17632/nd5kftbhyj.1


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.


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Edith Cowan University, University of Western Australia


Computer Vision Representation, Benchmarking, Multimodality, Image Analysis, Action Recognition


Higher Education Commission, Pakistan


Office of National Intelligence, Australian Govermenment