Difference between revisions of "ISSS608 2016-17 T3 Assign Chan En Ying Grace Methodology"
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[[ISSS608_2017-18_T3_Assign_Chan_En_Ying_Grace|<b><font size="3"><font color="#000000">Background</font></font></b>]] | [[ISSS608_2017-18_T3_Assign_Chan_En_Ying_Grace|<b><font size="3"><font color="#000000">Background</font></font></b>]] | ||
− | | style="font-family:Arial; font-size:100%; solid # | + | | style="font-family:Arial; font-size:100%; solid #1B338F; background:#E6E6FA; text-align:center;" width="15%" | |
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− | [[ | + | [[ISSS608_2017-18_T3_Assign_Chan_En_Ying_Grace_Methodology|<b><font size="3"><font color="#000000">Methodology</font></font></b>]] |
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− | [[ | + | [[ISSS608_2017-18_T3_Assign_Chan_En_Ying_Grace_Did Rose Pipit kick the bucket?|<b><font size="3"><font color="#000000">Did Rose Pipit kicketh the bucket?</font></font></b>]] |
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− | [[ | + | [[ISSS608_2017-18_T3_Chan_En_Ying_Grace_Which song belongs to thee?| <b><font size="3"><font color="#000000">Which song belongs to thee?</font></font></b>]] |
| style="font-family:Arial; font-size:100%; solid #1B338F; background:#E6E6FA; text-align:center;" width="20%" | | | style="font-family:Arial; font-size:100%; solid #1B338F; background:#E6E6FA; text-align:center;" width="20%" | | ||
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− | [[ | + | [[ISSS608_2017-18_T3_Chan_En_Ying_Grace_Conclusion| <b><font size="3"><font color="#000000">Conclusion</font></font></b>]] |
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<!-- END OF TOOLS--> | <!-- END OF TOOLS--> | ||
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==<font size="5"><font color="#000000">'''Approach Taken'''</font></font>== | ==<font size="5"><font color="#000000">'''Approach Taken'''</font></font>== | ||
− | The following outlines the | + | The following outlines the 6 broad steps used for the analysis - Data Cleaning, Data Preparation, Geospatial Visualisation, Statistical Confirmation, Audio Processing and Audio Visualisation. |
<div style="margin:0px; padding: 2px; background: #E6E6FA; font-family: Arial; border-radius: 1px; text-align:left"> | <div style="margin:0px; padding: 2px; background: #E6E6FA; font-family: Arial; border-radius: 1px; text-align:left"> | ||
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* Then add in the dumping site & adjust the colour/size | * Then add in the dumping site & adjust the colour/size | ||
* So that we can visualize the clusters relative to the dumping site | * So that we can visualize the clusters relative to the dumping site | ||
+ | |||
+ | |- | ||
+ | | | ||
+ | 6. | ||
+ | || | ||
+ | <b>Audio Visualisation</b> | ||
+ | || | ||
+ | <b>i. Amplitude Envelope Plot</b> | ||
+ | * Use diffenv() to plot the envelopes of the amplitutde plots | ||
+ | * Do this for all the 15 test birds | ||
+ | * Do this for 5 training birds per species and select most representative plot as your ‘dictionary’ | ||
+ | |||
+ | |||
+ | <b>ii. Oscillogram Plot</b> | ||
+ | * Use seewave package to plot osciilogram | ||
+ | * Do this for all the 15 test birds | ||
+ | * Do this for 5 of the training birds, per species and select most representative plot as your ‘dictionary’ | ||
+ | |||
+ | |||
+ | <b>iii. Distribution of audio parameters, using Trellis Plot</b> | ||
+ | * Out of the 15 attributes available after extracting the dataframe from the .wav files, the following 7 will be used for analysis: | ||
+ | * dom_median,HNR_median, meanFreq_median, peakFreq_median, pitch_median, pitchAutocor_median, pitchSpec_median | ||
+ | * These were selected as they vary across the species more, than the other of the 8 variables | ||
+ | * Use ggplot() to plot a trellis plot using the 19 training species | ||
+ | * Label the mean | ||
+ | * Use ggplot() to insert the 15 testing birds | ||
+ | * Visualise and identify the top 3 closest species, per parameter | ||
+ | * Select the species based on no. of parameters closest to the training mean | ||
|} | |} | ||
</div> | </div> | ||
<br> | <br> |
Latest revision as of 22:42, 22 June 2018
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Tools
R is the primary tool used in this analysis. The following lists the packages used for the project’s scope - for data cleaning, data visualisation, geospatial analysis and audio processing.
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Approach Taken
The following outlines the 6 broad steps used for the analysis - Data Cleaning, Data Preparation, Geospatial Visualisation, Statistical Confirmation, Audio Processing and Audio Visualisation.
Step |
Approach |
Description |
1. |
Data Understanding |
i. Read in Raster Layer (Lekagul Roadways Map)
class : RasterLayer
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2. |
Data Cleaning |
i. Import two CSV Files (Birds)
iii. Data Manipulation
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3. |
Geospatial Visualisation |
Spatial Point Pattern Visualisation (Density-Based Measure) i. Prepare polygon layer
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4. |
Statistical Confirmation |
Spatial Point Pattern Analysis (Distance-Based Measure) i. Quadrat Analysis
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5. |
Audio Processing |
i. Data Preparation (Density-Based Measure)
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6. |
Audio Visualisation |
i. Amplitude Envelope Plot
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