Classification of Infant Crying in Real-World Home Environments Using Deep Learning

05/12/2020
by   Xuewen Yao, et al.
0

In the domain of social signal processing, automated audio recognition is a promising avenue for researchers interested in accessing daily behaviors that can contribute to wellbeing and mental health outcomes. However, despite remarkable advances in mobile computing and machine learning, audio behavior detection models are largely constrained to data collected in controlled settings such as labs or call centers. This is problematic as it means their performance is unlikely to adequately generalize to real-world applications. In the current paper, we present a model combining deep spectrum and acoustic features to detect and classify infant distress vocalizations in real-world data. To develop our model, we collected and annotated a large dataset of over 780 hours of real-world audio data via a wearable audio recorder worn by infants for up to 24 hours in their natural home environments. Our model has F1 score of 0.597 relative to an F1 score of 0.166 achieved by real-world state-of-practice infant distress classifiers and an F1 score of 0.26 achieved by state-of-the-art, real-world infant distress classifiers published last year in Interspeech's paralinguistic challenge. Impressively, it also achieves an F1 score within 0.1 of state-of-the-art infant distress classifiers developed and tested using laboratory quality data.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/23/2019

Self-Attentive Model for Headline Generation

Headline generation is a special type of text summarization task. While ...
research
06/27/2021

A Machine Learning Model for Early Detection of Diabetic Foot using Thermogram Images

Diabetes foot ulceration (DFU) and amputation are a cause of significant...
research
07/28/2021

Deep learning based cough detection camera using enhanced features

Coughing is a typical symptom of COVID-19. To detect and localize coughi...
research
10/31/2019

Adversarial Music: Real World Audio Adversary Against Wake-word Detection System

Voice Assistants (VAs) such as Amazon Alexa or Google Assistant rely on ...
research
11/17/2019

NeckSense: A Multi-Sensor Necklace for Detecting Eating Activities in Free-Living Conditions

We present the design, implementation, and evaluation of a multi-sensor ...
research
07/25/2019

An EMG-based Eating Behaviour Monitoring System with Haptic Feedback to Promote Mindful Eating

Mindless eating, or the lack of awareness of the food we are consuming, ...
research
05/30/2023

GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks

Label errors have been found to be prevalent in popular text, vision, an...

Please sign up or login with your details

Forgot password? Click here to reset