Automatic detection of potentially ineffective verbal communication for training through simulation in neonatology

Coro, Gianpaolo; Bardelli, Serena; Cuttano, Armando; Fossati, N.

doi:10.1007/s10639-022-11000-z

Cited by 4 publications

(6 citation statements)

References 22 publications

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“…Our workflow had an overall moderate agreement with the expert's annotations across all cases, with a 76.4% accuracy and 70% F1. These results, compared to other systems [52,62], indicate that it was reasonably good considering the SNR ranging between 2.4 and 20.…”

Section: Performancesupporting

confidence: 50%

“…Energy is here intended as the squared sum of the samples of an audio-signal segment divided by the number of signalsamples (signal-segment power). Tone units have been used in spoken dialogue processing and to improve ASR performance because they mostly contain complete sentences [61,62]. Our signal segmentation module uses a fast algorithm for tone unit detection [61], which requires setting a tone unit window length (in ms) parameter to calculate segment energy and find tone unit boundaries.…”

Section: Signal Segmentationmentioning

confidence: 99%

“…As the output, the process divides the initial audio recording into shorter recordings. Tone units with lengths under 1s are discarded as containing either noise or sound bursts [62].…”

Section: Signal Segmentationmentioning

confidence: 99%

“…Tone unit segmentation removes short islands of noise, low-energy signal segments, and sound bursts. The segments left can contain continuous adult speech and infant cry, overlapped with machine noise within a constant or increasing energy profile [61,62]. As the next step, our workflow analyses each tone unit independently of the other.…”

Section: Energy and Pitch Extractionmentioning

confidence: 99%

“…the short-windowed spectrogram contains too much noise to distinguish between speech, noise, and infant cry automatically [63]. Therefore, the analysis would be better conducted at a syllabic scale (100-250 ms), where the spectrum is more robust to high noise levels and the study of energy modulations is more reliable [62,64,65].…”

Section: Energy and Pitch Extractionmentioning

confidence: 99%

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A self-training automatic infant-cry detector

Coro

Bardelli²,

Cuttano³

et al. 2022

Neural Comput & Applic

Self Cite

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Infant cry is one of the first distinctive and informative life signals observed after birth. Neonatologists and automatic assistive systems can analyse infant cry to early-detect pathologies. These analyses extensively use reference expert-curated databases containing annotated infant-cry audio samples. However, these databases are not publicly accessible because of their sensitive data. Moreover, the recorded data can under-represent specific phenomena or the operational conditions required by other medical teams. Additionally, building these databases requires significant investments that few hospitals can afford. This paper describes an open-source workflow for infant-cry detection, which identifies audio segments containing high-quality infant-cry samples with no other overlapping audio events (e.g. machine noise or adult speech). It requires minimal training because it trains an LSTM-with-self-attention model on infant-cry samples automatically detected from the recorded audio through cluster analysis and HMM classification. The audio signal processing uses energy and intonation acoustic features from 100-ms segments to improve spectral robustness to noise. The workflow annotates the input audio with intervals containing infant-cry samples suited for populating a database for neonatological and early diagnosis studies. On 16 min of hospital phone-audio recordings, it reached sufficient infant-cry detection accuracy in 3 neonatal care environments (nursery—69%, sub-intensive—82%, intensive—77%) involving 20 infants subject to heterogeneous cry stimuli, and had substantial agreement with an expert’s annotation. Our workflow is a cost-effective solution, particularly suited for a sub-intensive care environment, scalable to monitor from one to many infants. It allows a hospital to build and populate an extensive high-quality infant-cry database with a minimal investment.

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Section: Performancesupporting

confidence: 50%

Section: Signal Segmentationmentioning

confidence: 99%

“…As the output, the process divides the initial audio recording into shorter recordings. Tone units with lengths under 1s are discarded as containing either noise or sound bursts [62].…”

Section: Signal Segmentationmentioning

confidence: 99%

Section: Energy and Pitch Extractionmentioning

confidence: 99%

Section: Energy and Pitch Extractionmentioning

confidence: 99%

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A self-training automatic infant-cry detector

Coro

Bardelli²,

Cuttano³

et al. 2022

Neural Comput & Applic

Self Cite

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Validación psicométrica de la «Escala de habilidades comunicacionales aplicada por paciente estandarizado» para la simulación clínica

Lermanda Peña,

Hernández Cortina

2024

Educación Médica

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Neural Networks-Based Approach for Detecting and Filtering Misleading Audio Segments for Classroom Automatic Transcription

Hewstone,

Araya

2023

Preprint

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Audio recording in classrooms is a common practice in educational research, with applications ranging from detecting classroom activities to analysing student behaviour. Previous research has employed neural networks for classroom activity detection and speaker role identification. However, these recordings are often affected by background noise that can hinder further analysis, and the literature has only sought to identify noise with general filters and not specifically designed for classrooms. Although the use of high-end microphones and environmental monitoring can mitigate this problem, these solutions can be costly and potentially disruptive to the natural classroom environment. In this context, we propose the development of a neural network model that can specifically detect and filter out background noise in classroom recordings. This model would allow the use of lower quality recordings without compromising analysis capability, thus facilitating data collection in natural educational environments and reducing the costs associated with high-end recording equipment.

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Automatic detection of potentially ineffective verbal communication for training through simulation in neonatology

Cited by 4 publications

References 22 publications

A self-training automatic infant-cry detector

A self-training automatic infant-cry detector

Validación psicométrica de la «Escala de habilidades comunicacionales aplicada por paciente estandarizado» para la simulación clínica

Neural Networks-Based Approach for Detecting and Filtering Misleading Audio Segments for Classroom Automatic Transcription

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