PyMAiVAR: An open-source Python suit for audio-image representation in human action recognition[Formula presented]

Muhammad Bilal Shaikh; Douglas Chai; Syed Mohammed Shamsul Islam; Naveed Akhtar

doi:10.1016/j.simpa.2023.100544

PyMAiVAR: An open-source Python suit for audio-image representation in human action recognition[Formula presented]

Muhammad Bilal Shaikh, Douglas Chai, Syed Mohammed Shamsul Islam, Naveed Akhtar

Computer Science and Software Engineering

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

We present PyMAiVAR, a versatile toolbox that encompasses the generation of image representations for audio data including Wave plots, Spectral Centroids, Spectral Roll Offs, Mel Frequency Cepstral Coefficients (MFCC), MFCC Feature Scaling, and Chromagrams. This wide-ranging toolkit generates rich audio-image representations, playing a pivotal role in reshaping human action recognition. By fully exploiting audio data's latent potential, PyMAiVAR stands as a significant advancement in the field. The package is implemented in Python and can be used across different operating systems.

Original language	English
Article number	100544
Journal	Software Impacts
Volume	17
DOIs	https://doi.org/10.1016/j.simpa.2023.100544
Publication status	Published - Sept 2023

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10.1016/j.simpa.2023.100544Licence: CC BY

Cite this

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title = "PyMAiVAR: An open-source Python suit for audio-image representation in human action recognition[Formula presented]",

abstract = "We present PyMAiVAR, a versatile toolbox that encompasses the generation of image representations for audio data including Wave plots, Spectral Centroids, Spectral Roll Offs, Mel Frequency Cepstral Coefficients (MFCC), MFCC Feature Scaling, and Chromagrams. This wide-ranging toolkit generates rich audio-image representations, playing a pivotal role in reshaping human action recognition. By fully exploiting audio data's latent potential, PyMAiVAR stands as a significant advancement in the field. The package is implemented in Python and can be used across different operating systems.",

keywords = "Computer vision, Human action recognition, Image representations, Multimodal",

author = "Shaikh, {Muhammad Bilal} and Douglas Chai and Islam, {Syed Mohammed Shamsul} and Naveed Akhtar",

note = "Funding Information: This work is jointly supported by Edith Cowan University (ECU), Australia and Higher Education Commission (HEC) of Pakistan under Project #PM/HRDI-UESTPs/UETs-I/Phase-1/Batch-VI/2018 . Dr. Akhtar is a recipient of Office of National Intelligence National Intelligence Postdoctoral Grant # NIPG-2021–001 funded by the Australian Government . Publisher Copyright: {\textcopyright} 2023 The Author(s)",

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language = "English",

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journal = "Software Impacts",

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AU - Chai, Douglas

AU - Islam, Syed Mohammed Shamsul

AU - Akhtar, Naveed

N1 - Funding Information: This work is jointly supported by Edith Cowan University (ECU), Australia and Higher Education Commission (HEC) of Pakistan under Project #PM/HRDI-UESTPs/UETs-I/Phase-1/Batch-VI/2018 . Dr. Akhtar is a recipient of Office of National Intelligence National Intelligence Postdoctoral Grant # NIPG-2021–001 funded by the Australian Government . Publisher Copyright: © 2023 The Author(s)

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N2 - We present PyMAiVAR, a versatile toolbox that encompasses the generation of image representations for audio data including Wave plots, Spectral Centroids, Spectral Roll Offs, Mel Frequency Cepstral Coefficients (MFCC), MFCC Feature Scaling, and Chromagrams. This wide-ranging toolkit generates rich audio-image representations, playing a pivotal role in reshaping human action recognition. By fully exploiting audio data's latent potential, PyMAiVAR stands as a significant advancement in the field. The package is implemented in Python and can be used across different operating systems.

AB - We present PyMAiVAR, a versatile toolbox that encompasses the generation of image representations for audio data including Wave plots, Spectral Centroids, Spectral Roll Offs, Mel Frequency Cepstral Coefficients (MFCC), MFCC Feature Scaling, and Chromagrams. This wide-ranging toolkit generates rich audio-image representations, playing a pivotal role in reshaping human action recognition. By fully exploiting audio data's latent potential, PyMAiVAR stands as a significant advancement in the field. The package is implemented in Python and can be used across different operating systems.

KW - Computer vision

KW - Human action recognition

KW - Image representations

KW - Multimodal

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U2 - 10.1016/j.simpa.2023.100544

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VL - 17

JO - Software Impacts

JF - Software Impacts

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PyMAiVAR: An open-source Python suit for audio-image representation in human action recognition[Formula presented]

Abstract

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Other files and links

Fingerprint

MFFCs for Multi-class Human Action Analysis : A Benchmark Dataset

Waveplot-based Dataset for Multi-class Human Action Analysis

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

Cite this

PyMAiVAR: An open-source Python suit for audio-image representation in human action recognition[Formula presented]

Abstract

Access to Document

Other files and links

Fingerprint

Datasets

MFFCs for Multi-class Human Action Analysis : A Benchmark Dataset

Waveplot-based Dataset for Multi-class Human Action Analysis

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

Cite this