Automatic classification of infant vocalization sequences with convolutional neural networks

Anders, Franz; Hlawitschka, Mario; Fuchs, Mirco

doi:10.1016/j.specom.2020.03.003

Cited by 16 publications

(12 citation statements)

References 19 publications

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“…A neural network was selected because of the minimal pre-processing and data reduction required. Additionally, neural networks have proven highly capable in sound classification tasks [46][47][48] . The convolutional neural network ResNet-50 was chosen to be adapted via transfer learning because of its performance efficiency 49 and proven application in this field 47 .…”

Section: Automated Classificationmentioning

confidence: 99%

Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production

Briefer

Sypherd

Linhart

et al. 2022

Sci Rep

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Vocal expression of emotions has been observed across species and could provide a non-invasive and reliable means to assess animal emotions. We investigated if pig vocal indicators of emotions revealed in previous studies are valid across call types and contexts, and could potentially be used to develop an automated emotion monitoring tool. We performed an analysis of an extensive and unique dataset of low (LF) and high frequency (HF) calls emitted by pigs across numerous commercial contexts from birth to slaughter (7414 calls from 411 pigs). Our results revealed that the valence attributed to the contexts of production (positive versus negative) affected all investigated parameters in both LF and HF. Similarly, the context category affected all parameters. We then tested two different automated methods for call classification; a neural network revealed much higher classification accuracy compared to a permuted discriminant function analysis (pDFA), both for the valence (neural network: 91.5%; pDFA analysis weighted average across LF and HF (cross-classified): 61.7% with a chance level at 50.5%) and context (neural network: 81.5%; pDFA analysis weighted average across LF and HF (cross-classified): 19.4% with a chance level at 14.3%). These results suggest that an automated recognition system can be developed to monitor pig welfare on-farm.

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Section: Automated Classificationmentioning

confidence: 99%

Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production

Briefer

Sypherd

Linhart

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…We finally selected the best configurations of each type and compared their performances in a contest-like setup. This research substantially expands the scope of our previous work [30] in which we investigated ordinary VGG-like CNNs for infant vocalization classification, albeit on a different dataset and with different target classes; In the present study we further investigated various network types, covered a greater and more diverse search space, and used the competition's dataset so that our results can be compared to competing approaches.…”

Section: Introductionmentioning

confidence: 82%

Comparison of Artificial Neural Network Types for Infant Vocalization Classification

Anders

Hlawitschka

Fuchs

2021

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…The study in [ 19 ] implemented convolutional neural network (CNN) for classifying the infant vocal sequences. The classes identified were, namely, “crying,” “fussing,” “babbling,” “laughing,” and “vegetative vocalization.” The audio segments were represented as spectrograms and fed into the conventional CNN.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Classification of Neonates Cry Using Extreme Gradient Boosting-Assisted Grouped-Support-Vector Network

Chang

Bhattacharya

Vincent

et al. 2021

Journal of Healthcare Engineering

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The cry is a loud, high pitched verbal communication of infants. The very high fundamental frequency and resonance frequency characterize a neonatal infant cry having certain sudden variations. Furthermore, in a tiny duration solitary utterance, the cry signal also possesses both voiced and unvoiced features. Mostly, infants communicate with their caretakers through cries, and sometimes, it becomes difficult for the caretakers to comprehend the reason behind the newborn infant cry. As a result, this research proposes a novel work for classifying the newborn infant cries under three groups such as hunger, sleep, and discomfort. For each crying frame, twelve features get extracted through acoustic feature engineering, and the variable selection using random forests was used for selecting the highly discriminative features among the twelve time and frequency domain features. Subsequently, the extreme gradient boosting-powered grouped-support-vector network is deployed for neonate cry classification. The empirical results show that the proposed method could effectively classify the neonate cries under three different groups. The finest experimental results showed a mean accuracy of around 91% for most scenarios, and this exhibits the potential of the proposed extreme gradient boosting-powered grouped-support-vector network in neonate cry classification. Also, the proposed method has a fast recognition rate of 27 seconds in the identification of these emotional cries.

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Automatic classification of infant vocalization sequences with convolutional neural networks

Cited by 16 publications

References 19 publications

Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production

Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production

Comparison of Artificial Neural Network Types for Infant Vocalization Classification

An Efficient Classification of Neonates Cry Using Extreme Gradient Boosting-Assisted Grouped-Support-Vector Network

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