presentation

Chirpdetection poster script.md

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+---
+date: 2023-01-26
+author: Patrick Weygoldt
+type: talk
+speakers:
+  - name: 
+    affiliation: 
+  - name: 
+    affiliation: 
+aliases: 
+tags: 
+---
+# Chirp detection poster script
+
+## 10 minute presentation
+
+introduction:
+- Project goal: Develop a chirp detection algorithm
+- What are chirps?
+	- short frequency excursions in ms range of EOD (electric organ discharge) of weakly el. fish.
+- Show plot:
+		- spectrogram of the EODf of two fish (two lines)
+			- frequency resolution 150Hz
+			- nfft: number of windows/datapoints over which the Fourier transform is performed
+		- frequency over time is shown
+		- color indicates power
+		- chirp = upper line, frequency increases shortly
+ - Problem:
+		- to resolve chirps on the time domain, frequency domain too coarse
+		- if lower fish chirps it becomes even harder
+		=> time-frequency uncertainty problem (general)
+- Goal: Improve existing detection methods to detect and assign chirps for electric recordings with n fish
+
+Chirp detection algorithm
+Availabe data:
+1. Raw electrical signal (EOD of multiple fish) over n electrodes (n = 11)
+2. Tracked frequency bands on spectrogram (pre-tracked): just as in upper right plot, but with lower sampling rate (3Hz)
+	- for one frequency we want the electrode on which the power of the f is the greatest
+	- power of the strongest of 11 electrodes for each frequencypoint in time was used to track the frequency band
+	- we cannot track freq on spectrogram's time resolution is too low for detecting chirps if you wanna distinguish between the fish
+Feature extraction (in 5s rolling window):
+1. Bandpass filter around the tracked frequency band for one individual (+-5Hz)
+	- first subplot grey, red = envelope of filtered baseline
+2. Dynamic bandpass filter above baseline (+-5Hz) = 2nd subplot, gray filtered search frequency, orange = envelope
+	- dynamic search window above the current fish of interest
+		- Why dynamic: if another fish has a higher frequency, we need to find a window without another fish to be able to detect the chirp
+			- window above fish: look if there's another fish (array stuff), True/false thing, find longest subarray
+		- chirps excursions always increase the frequency and decrease the amplitude
+		- to find chirp, we need to search above the fish and look for break down in amplitude
+		- no peaks in filtered above = no chirp
+		- amplitude break down of baseline can have multiple reasons (e.g. fish swims away, stone)
+3. Instantaneous frequency of baseline = 3rd subplot, gray filtered inst., yellow = envelope
+	- calculated on filtered raw data
+	- get zero crossings of each period and calculate frequency manually
+	- we know that chirps are increases in frequency, here we look at the frequency feature of chirps
+Peak detection:
+1. Detect peaks on bandpass filtered and inverted baseline envelope (lower red line)
+2. Detect peaks on bandpass filtered search frequency (lower orange line)
+3. Detect peaks on absolute inst. freq (lower yellow line)
+	- Peak prominence: Minimal distance from highest peak to next peak
+Peak classification:
+- all three features have to be present at once in a 20ms window (appr. chirp length) in strongest electrodes
+- mean of peak timestamps of features is saved as chirp timestamp
+
+Chirps in dyadic competitions
+1. Competition experiment by Til Raab:
+	- two fish compete for one shelter
+	- 6h recording, 3h light, 3h dark
+	- electrical and video recordings
+	- with video recordings, behavior was tracked and assigned to an antagonistic category: Chasing (on- and offset) and physical contacts
+- we did behavioral analysis with the detected chirps of our algorithm
+2. Plot: Contact an chasing event timepoints, chirps of both fish, tracked frequency bands
+	- from literature: Chirps assumed as submissive signal by loser fish
+4. Winner Loser boxplot
+	- chirps counts for winner and loser (n=22 recordings with winner and loser)
+	- loser tends to chirp more (Wilcoxon not significant, but trend with 0.054)
+	- white lines are paired fish for competition
+5. Size difference plot
+	- Literature: Larger wish usually wins. (Larger resource holding potential theory, Till with rises)
+	- The smaller the size difference between fish, the more chirps are emitted taken winner and loser together
+	- correlation within winners and losers are not significant
+	- n = 21 because one recording with equal fish size was excluded
+6. Frequency plot
+	- Literature: Males are more aggressive and chirp more, males have a lower EODf
+	- EODf has no effect on the competition outcome
+
+Chirps emitted by loser fish might stop chasing events
+- Chirps were centered around the timestamps of each event in a +-60s time window (for each category and each recording)
+- kernel density estimation of centered chirps (gaussian kernel with 2s width and 10ms resolution)
+- We show some example plots
+1. First plot: No correlation case for chasing offset
+	- no correlation between chirping and the offset
+	- this was the case for most recordings and all events
+2. Second plot: Correlation case for chirping and offset
+	- For some few dyads/individuals, chirp rate increases drastically before the chasing offset
+	- also slightly visible before chasing onsets
+	- no correlation for physical contacts
+3. Third plot: Time of chasing events in the night VS the chirps during the chasing events and during night
+	- fraction of chirps during chasings is not increased relative to the fraction of chasing events overall
+	- Chirps do not seem to have an increased significance for chasing events
+	- only for some few dyads the chirp rate increased during chasings
+- Gray/black areas:
+	- bootstrapped data (n = 50)
+	- all chirps for one recording during the night (because there more chirps)
+	- all shuffled chirps again centered around event and convolved
+
+Conclusion:
+- First tests indicate that our algorithm is able to detect chirps in recordings of multiple fish
+- Algorithm results were applied on behavioral data for further analysis
+
+
+## 2 Minute presentation
+
+Introduction:
+- Project goal: Develop a chirp detection algorithm
+- What are chirps?
+	- Short frequency excursions in ms range of EOD (electric organ discharge) of weakly el. fish.
+	-  to resolve chirps on the time domain, frequency domain too coarse, especially for multiple fish
+- Goal: Improve existing detection methods
+
+Detection algorithm
+- Improved existing detection methods by extracting 3 features that change during a chirp but are not limited by the time frequency uncertainty
+	1. Amplitude drop of EOD (show trough)
+	2. Peaks of instantaneous frequency of EOD
+	3. Peaks in the dynamically adjusted frequency band above the fish's baseline EODf (special).
+- Detected and classified peaks are chirp times
+
+Application: Chirps during competition
+- Detected over 10000 chirps in real data from a competition experiment
+- Analysis of the relationship of chirps and competition events
+- Fish competed for a shelter
+- Were able to replicate some findings from literature
+	- e.g. loser fish tend to chirp more
+	- Other findings are not that clear and require the consideration of more factors, e.g. sex
+- We explored how the chirp rate changes during onsets and offsets of chasing events
+	- For some recordings, chirping increased strongly before the offset of a chasing, for some nothing happens
+	- The number of chirps during chasings is only elevated for some dyads
+
+Conclusion
+- Algorithm can be used to detect chirps
+- We could replicate some literature findings and motivate further examination