Difference between revisions of "ISSS608 2017-18 T3 Kiriti Yelamanchali Q2"
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| − | For analysis, | + | == Deep Learning Model to recognize bird calls== |
| + | For this analysis, a deep learning model is built to recognize the bird calls. | ||
| + | * Python library librosa is ised for this purpose | ||
| + | |||
| + | 1. The cleaned csv from data preparation stage is loaded into the pandas dataframe. | ||
| + | [[File:Kiriti ml 1.png| 800 px]] | ||
| + | |||
| + | 2. A new dataframe column is created to match the mp3 file names in the data, after a bit of cleaning. | ||
| + | [[File:Kiriti ml2.png | 800 px]] | ||
| + | |||
| + | 3. The wavelet spectographs of one bird are observed across different qualities, to have a visual representation of the quality | ||
| + | [[File:Kiriti pipit comaprisions.png]|600 px] | ||
| + | |||
| + | It is evident that, the Qualities marked as A and B are useful in terms of model training. | ||
Revision as of 23:22, 8 July 2018
VAST CHALLENGE 2018: MC1
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Deep Learning Model to recognize bird calls
For this analysis, a deep learning model is built to recognize the bird calls.
- Python library librosa is ised for this purpose
1. The cleaned csv from data preparation stage is loaded into the pandas dataframe.
2. A new dataframe column is created to match the mp3 file names in the data, after a bit of cleaning.
3. The wavelet spectographs of one bird are observed across different qualities, to have a visual representation of the quality [[File:Kiriti pipit comaprisions.png]|600 px]
It is evident that, the Qualities marked as A and B are useful in terms of model training.
