Automated identification of chicken distress vocalizations using deep learning models

Abstract

The annual global production of chickens exceeds 25 billion birds, which are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an 'iceberg indicator' of chicken welfare. However, to date, the identification of distress calls largely relies on manual annotation, which is very labour-intensive and time-consuming. Thus, a novel convolutional neural network-based model, light-VGG11, was developed to automatically identify chicken distress calls using recordings (3363 distress calls and 1973 natural barn sounds) collected on an intensive farm. The light-VGG11 was modified from VGG11 with significantly fewer parameters (9.3 million versus 128 million) and 55.88% faster detection speed while displaying comparable performance, i.e. precision (94.58%), recall (94.89%), F1-score (94.73%) and accuracy (95.07%), therefore more useful for model deployment in practice. To additionally improve light-VGG11's performance, we investigated the impacts of different data augmentation techniques (i.e. time masking, frequency masking, mixed spectrograms of the same class and Gaussian noise) and found that they could improve distress calls detection by up to 1.52%. Our distress call detection demonstration on continuous audio recordings, shows the potential for developing technologies to monitor the output of this call type in large, commercial chicken flocks.

Keywords: animal welfare; bioacoustics; convolutional neural networks; data augmentation; precision livestock farming.

Figure 1.

The layout of the chicken house and microphone placement (84 m length, 11.3 m width and 3.2 m height).

Figure 2.

An example of how the data were annotated. The squares in red highlight the distress calls, while the square in white shows what would be termed ‘natural barn sounds’ (the absence of work sounds and distress calls).

Figure 3.

The overall flow chart of the audio classification method. (a) Raw waveform signals. (b) Log-Mel spectrograms. (c) Fivefold cross-validation. (d) The architecture of light-VGG11. (e) Predictions.

Figure 4.

Log-Mel spectrograms samples of (a) natural barn sounds and (b) distress calls.

Figure 5.

The original log-Mel spectrogram and its four transformed versions. (a) Original input. (b) Input with time masking. (c) Input with frequency masking. (d) Input with SpecSameClassMix. (e) Input with Gaussian noise.

Figure 6.

The recall confusion matrix of light-VGG11 without data augmentation (a) and with four data augmentation strategies: time masking (b), frequency masking (c), SpecSameClassMix (d) and Gaussian noise (e).

Figure 7.

Frequency and cumulative time of the distress call events with various durations within two continuous 10 min audio episodes.