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Discerning pig screams in production environments.

Vandermeulen J, Bahr C, Tullo E, Fontana I, Ott S, Kashiha M, Guarino M, Moons CP, Tuyttens FA, Niewold TA, Berckmans D - PLoS ONE (2015)

Bottom Line: To achieve this, 7 hours of labelled data from 24 pigs was used.The developed detection method attained 72% sensitivity, 91% specificity and 83% precision.As a result, the detection method showed that screams contain the following features discerning them from other sounds: a formant structure, adequate power, high frequency content, sufficient variability and duration.

View Article: PubMed Central - PubMed

Affiliation: M3-BIORES-Measure, Model & Manage Bioresponses, KU Leuven, Leuven, Belgium.

ABSTRACT
Pig vocalisations convey information about their current state of health and welfare. Continuously monitoring these vocalisations can provide useful information for the farmer. For instance, pig screams can indicate stressful situations. When monitoring screams, other sounds can interfere with scream detection. Therefore, identifying screams from other sounds is essential. The objective of this study was to understand which sound features define a scream. Therefore, a method to detect screams based on sound features with physical meaning and explicit rules was developed. To achieve this, 7 hours of labelled data from 24 pigs was used. The developed detection method attained 72% sensitivity, 91% specificity and 83% precision. As a result, the detection method showed that screams contain the following features discerning them from other sounds: a formant structure, adequate power, high frequency content, sufficient variability and duration.

No MeSH data available.


The FFT spectrograms and CGD spectrograms.The upper two figures show the same screams, the lower figures show the same sneeze. The left figures depict the spectrogram made from FFT while the figures on the right side depict the spectrogram made from CGD. The formant structure of a scream is visible (B). These formants are the whiter values in the CGD spectrogram. It is not straightforward to find the same structure in the FFT spectrogram. Because the difference between the formant value and the surrounding values is much bigger relative to the maximum and minimum values of the CGD compared to the FFT
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pone.0123111.g003: The FFT spectrograms and CGD spectrograms.The upper two figures show the same screams, the lower figures show the same sneeze. The left figures depict the spectrogram made from FFT while the figures on the right side depict the spectrogram made from CGD. The formant structure of a scream is visible (B). These formants are the whiter values in the CGD spectrogram. It is not straightforward to find the same structure in the FFT spectrogram. Because the difference between the formant value and the surrounding values is much bigger relative to the maximum and minimum values of the CGD compared to the FFT

Mentions: In order to calculate these transformations, sound data was divided into 30ms hamming windows [32] with a 15ms overlap. This duration was chosen similar to speech analysis in which 20-40ms windows are used.[33]. Calculating the transformations on each window provided time frequency information. An example is presented for both FFT and CGD in Fig 3. To further reduce FFT and CGD data, the frequency resolution was lowered into 24 Mel-spaced frequency bands. This experimental scale is used to resemble human perception of sound frequency, particularly fundamental frequency [34].


Discerning pig screams in production environments.

Vandermeulen J, Bahr C, Tullo E, Fontana I, Ott S, Kashiha M, Guarino M, Moons CP, Tuyttens FA, Niewold TA, Berckmans D - PLoS ONE (2015)

The FFT spectrograms and CGD spectrograms.The upper two figures show the same screams, the lower figures show the same sneeze. The left figures depict the spectrogram made from FFT while the figures on the right side depict the spectrogram made from CGD. The formant structure of a scream is visible (B). These formants are the whiter values in the CGD spectrogram. It is not straightforward to find the same structure in the FFT spectrogram. Because the difference between the formant value and the surrounding values is much bigger relative to the maximum and minimum values of the CGD compared to the FFT
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4414550&req=5

pone.0123111.g003: The FFT spectrograms and CGD spectrograms.The upper two figures show the same screams, the lower figures show the same sneeze. The left figures depict the spectrogram made from FFT while the figures on the right side depict the spectrogram made from CGD. The formant structure of a scream is visible (B). These formants are the whiter values in the CGD spectrogram. It is not straightforward to find the same structure in the FFT spectrogram. Because the difference between the formant value and the surrounding values is much bigger relative to the maximum and minimum values of the CGD compared to the FFT
Mentions: In order to calculate these transformations, sound data was divided into 30ms hamming windows [32] with a 15ms overlap. This duration was chosen similar to speech analysis in which 20-40ms windows are used.[33]. Calculating the transformations on each window provided time frequency information. An example is presented for both FFT and CGD in Fig 3. To further reduce FFT and CGD data, the frequency resolution was lowered into 24 Mel-spaced frequency bands. This experimental scale is used to resemble human perception of sound frequency, particularly fundamental frequency [34].

Bottom Line: To achieve this, 7 hours of labelled data from 24 pigs was used.The developed detection method attained 72% sensitivity, 91% specificity and 83% precision.As a result, the detection method showed that screams contain the following features discerning them from other sounds: a formant structure, adequate power, high frequency content, sufficient variability and duration.

View Article: PubMed Central - PubMed

Affiliation: M3-BIORES-Measure, Model & Manage Bioresponses, KU Leuven, Leuven, Belgium.

ABSTRACT
Pig vocalisations convey information about their current state of health and welfare. Continuously monitoring these vocalisations can provide useful information for the farmer. For instance, pig screams can indicate stressful situations. When monitoring screams, other sounds can interfere with scream detection. Therefore, identifying screams from other sounds is essential. The objective of this study was to understand which sound features define a scream. Therefore, a method to detect screams based on sound features with physical meaning and explicit rules was developed. To achieve this, 7 hours of labelled data from 24 pigs was used. The developed detection method attained 72% sensitivity, 91% specificity and 83% precision. As a result, the detection method showed that screams contain the following features discerning them from other sounds: a formant structure, adequate power, high frequency content, sufficient variability and duration.

No MeSH data available.