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Mining Mouse Vocalizations Jesin Zakaria Department of Computer Science and Engineering University of California Riverside.

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Presentation on theme: "Mining Mouse Vocalizations Jesin Zakaria Department of Computer Science and Engineering University of California Riverside."— Presentation transcript:

1 Mining Mouse Vocalizations Jesin Zakaria Department of Computer Science and Engineering University of California Riverside

2 124 Time (second) kHz 100 laboratory mice Mouse Vocalizations Figure 1: top) A waveform of a sound sequence produced by a lab mouse, middle) A spectrogram of the sound, bottom) An idealized version of the spectrogram

3 A X Q X P A X C X P The intution behind symbolizing the spectrogram Figure 3: The two fragments of data shown in Figure 2.bottom aligned to produce the maximum overlap. (Best viewed in color) Figure 4: The data shown in Figure 2 augmented by labeled syllables Figure1: top ) Two 0.5 second spectrogram representations of fragments of the vocal output of a male mouse. bottom ) Idealized (by human intervention) versions of the above 2

4 Time (second) kHz original idealized Background Figure 6: top) Original spectrogram, bottom) Idealized spectrogram (after thresholding and binarization) Figure 7: left) A real spectrogram of a mouse vocalization can be approximated by samples of handwritten Farsi digits (right). Some Farsi digits were rotated or transposed to enhance the similarity Time (sec) Figure 5: A snippet spectrogram that has seven syllables

5 I L SP connected components Figure 8: from left to right)snippet spectrogram, matrix corresponding to an idealized spectrogram I, matrix corresponding to the set of connected components L, mbrs of the candidate syllables Extracting syllables from spectrogram

6 I J K L M N O P A B C D E F G H a b c d e f g h i j k New Class Editing Ground truth I J K L M N O P A B C D E F G H Figure 9: Sixteen syllables provided by domain experts Figure 11: Ambiguity reduction of the original set of syllable classes. Representative examples from the reduced set of eleven classes are labeled as small letters

7 Editing Ground truth Figure 10: Thick/red curve represents the accuracy of classifying syllables of edited ground truth. Thin/blue curve represents the accuracy of classifying 692 labeled syllables using edited ground truth Adding more instances Classification Accuracy for edited ground truth for all the labeled syllables

8 Data mining Mouse Vocalizations ccccccgc eccccccc ecccccc ciaciaci dcibfcd ddcibfcd ccccccgc Figure 12: A clustering of eight snippets of mouse vocalization spectrograms using the string edit distance on the extracted syllables (spectrograms are rotated 90 degrees for visual clarity) Figure 13: A clustering of the same eight snippets of mouse vocalization shown in Figure 12 using the correlation method. The result appears near random Clustering mouse vocalizations

9 c c c c query image Data mining Mouse Vocalizations Similarity search / Query by content Figure 15: top) The query image from [2] was transcribed to cccc. Similar patterns are found in CT (first row) and KO (second row) mouse vocalizations in our collection Figure 14: top) A query image from [1], The syllable labels have been added by our algorithm to produce the query ciabqciacia, bottom) the two best matches found in our dataset; corresponding symbolic strings are ciafqcicia and ciqbqcaacja, with edit distance 2 and 3, respectively [1] J. M. S. Grimsley, et al., Development of Social Vocalizations in Mice. PLoS ONE 6(3): e17460 (2011). [2] T. E. Holy, Z. Guo, Ultrasonic songs of male mice, PLoS Biol 3(12): e386, (2005). query image c c c a a i b q a ii ciafqcicia Edit dist 2 ciqbqcaacja Edit dist 3

10 944.7 – sec – sec motif Data mining Mouse Vocalizations Assessing Motif Significance using z-score 16 17

11 Overrepresented in Control Overrepresented in Knock-out Figure 18: Examples of contrast set phrases. top) Three examples of a phrase ciacia that is overrepresented in KO, appearing 24 times in KO but never in CT. bottom) Two examples of a phrase dccccc that appears 39 times in CT and just twice in KO Contrast set mining using information gain


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