The purpose of this study was to analyze the possibility of utilizing known
signal processing and machine learning algorithms to correlate environmental
data to chicken vocalizations. The specific musing to be analyzed consist of
not just one chicken's vocalizations but of a whole collective, it therefore
becomes a chatter problem. There have been similar attempts to create such a
correlation in the past but with singled out birds instead of a multitude. This
study was performed on broiler chickens (birds used in meat production).
One of the reasons why this correlation is useful is for the purpose of an
automated control system. Utilizing the chickens own vocalization to determine
the temperature, the humidity, the levels of ammonia among other environmental
factors, reduces, and might even remove, the need for sophisticated sensors.
Another factor that this study wanted to correlate was stress in the chickens
to their vocalization. This has great implications in animal welfare, to
guarantee that the animals are being properly take care off. Also, it has been
shown that the meat of non-stressed chickens is of much better quality than the
opposite.
The audio was filtered and certain features were extracted to predict stress.
The features considered were loudness, spectral centroid, spectral sparsity,
temporal sparsity, transient index, temporal average, temporal standard
deviation, temporal skewness, and temporal kurtosis.
In the end, out of all the features analyzed it was shown that the kurtosis
and loudness proved to be the best features for identifying stressed birds in
audio.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/44754 |
| Date | 24 May 2012 |
| Creators | Colón, Guillermo J. |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0021 seconds