diff --git a/Chirpdetection poster script.md b/Chirpdetection poster script.md new file mode 100644 index 0000000..3868fd3 --- /dev/null +++ b/Chirpdetection poster script.md @@ -0,0 +1,141 @@ +--- +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 \ No newline at end of file