The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, &amp; Mosquitoes

Schuller, Björn W.; Batliner, Anton; Amiriparian, Shahin; Bergler, Christian; Gerczuk, Maurice; Holz, Natalie; Larrouy-Maestri, Pauline; Bayerl, Sebastian P.; Riedhammer, Korbinian; Mallol-Ragolta, Adria; Pateraki, Maria; Coppock, Harry; Kiskin, Ivan; Sinka, Marianne; Roberts, Stephen

doi:10.48550/arxiv.2205.06799

Cited by 1 publication

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“…Alternatively, several researchers employ more sophisticated features extracted from specialized software like openSMILE [ 42 ], PRAAT [ 89 ], and the Computational Paralinguistics Challenge (ComParE) [ 90 ] datasets from Interspeech 2010, 2013, and 2016. In particular, openSMILE is extensively employed for audio and speech processing, enabling the generation of a wide array of features, including low-level descriptors (e.g., spectral and prosodic features), higher-level descriptors (e.g., emotion and speaker characteristics), and audio events (e.g., music, laughter, and speech).…”

Section: Voice/speech-based Analysis For Respiratory Diseases Identif...mentioning

confidence: 99%

Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review

Kapetanidis,

Kalioras,

Tsakonas

et al. 2024

Sensors

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Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases’ symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems.

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Section: Voice/speech-based Analysis For Respiratory Diseases Identif...mentioning

confidence: 99%

Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review

Kapetanidis,

Kalioras,

Tsakonas

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes

Cited by 1 publication

References 0 publications

Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review

Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review

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