Mice_ASD_Detection

Includes code file from several consecutive projects, aimed at detecting ASD symptoms in mice, using analysis of their squeaks

January 2022: Final improvements - after performing L1 + L2 regularization and further tuning of hyper-parameters, we achieved a final accuracy of 88%.

December 2021: Further Improvements - after syllable classification, we extracted additional features and developed an XGBoost model to predict genotype of each mouse, succeeding with accuracy of 81%.

October 2021: Starting move of files from google drive 'USV_Project_2021' to Github repository 'Mice_ASD_Detection'.
All files written previous to our project are zipped and saved as "Project_Freeze_Sep2021_code_files.zip"

File Descriptions

File name	Description	Archive/Version	Notes
complete_data_131221.xlsx	Complete file of tagged data of each syllable recording after segmentation and classification (done in previous projects). Used as input for feature_extraction.	Version: Final
final_classification.ipynb	XGBoost model - training and testing on processed_data_for_final_classification.csv. Predicts genotype of each mouse (WT / HT).	Version: Final
processed_data_for_final_classification.csv	Data AFTER feature extraction, ready to be input to final_classification.ipynb	Version: Final
audio_feature_extraction_REDUCTION_BY_RECORDING.ipynb	feature extraction from total_data UPDATED EM 020821.xlsx	Version: Final	In this file, all samples from each mouse are reduced to a single sample PER RECORDING NUMBER with mean features.
XGBmodel_09012022.pkl	trained model weights	Version: Final
Archive/Project_Freeze_Sep2021_code_files.zip	Archive of all previous projects before our own. Includes data and code files for segmentation and classification of syllables.	Archive
Archive/Statistics_generator.ipynb	Generates statistics over data (previous projects)	Archive
Archive/Transfer_learning_2nd.ipynb	Used to classify the syllables in each segmented recording (previous projects)	Archive
audio_feature_extraction.ipynb	feature extraction from total_data UPDATED EM 020821.xlsx.	Archive	In this file, all samples from each mouse are reduced to a single sample with mean features.
total_data UPDATED EM 020821.xlsx	Tagged data of each syllable recording after segmentation and classification (done in previous projects). Used as input for feature_extraction.	Archive	Original data used early in the project. Later replaced by complete_data_131221.xlsx
Project_Presentation_Slides.pptx	Final presentation of the project to SIPL lab staff @ Technion	Version: Final
Project_Report_Hebrew.pdf	Project report submitted of the project to SIPL lab staff @ Technion	Version: Final

Project Result regeneration:

- Syllable Classification (previous projects):

• run transfer_learning_2nd.ipynb

- Final Classification:

• run audio_feature_extraction_REDUCTION_BY_RECORDING.ipynb
  • extracts features from total_data UPDATED EM 020821.xlsx
• then run final_classification.ipynb.

Google Drive + Code files info (written by: Guy Lavi & Vered Wolfman):

1. E: + Recordings_Ella_Ayelet:

Recordings of the mice, along with the syllable classification in the excel files.
The recordings are filed by the mother's name, with sub-directories named by the cub's name. Inside are the recordings.

Some E: sub-directories have intermediate folders, such as 'day' or 'session'.  

2. Segmented syllables:

Two folders, ending with 'syllable', include segmented recordings of one syllable each, at length of 0.25 seconds, organized according to the excel files.
There are also .npy files, containing an array of the spectograms of the syllables, and an array of mathching tags.

3. Codes:

The attached codes are made to create the data: segmentation of the recordings and saving in the folders, and creation of the .npy files.
Some code files contain classification models we implemented, or were implemented in previous projects.

April 2020: As of today, the codes are not organized and contain irrelevant fields, or ones meant to display for the user.

• data_creation - uses the excel file /TODO: find name of file/ to trim syllables from full recording and save separate copy of the syllable only.
• spect_creation - uses the trimmed syllables folder to create an array of spectograms (+ filtering) for the model training.
• statistics_generator - performs the above code files, but the saved array contains and object for each full recording with its details and the model's classification of its syllables
• statistics test - performs chi-squared tests and creates the graphs for the syllable distribution
• transfer_learning_2 - creates and trains the syllable-classification model, and saves its trained weights in 'model_weights.h5'

4. Data arrangement for statistics:

In order to create statistics, additional to the ones made by Guy & Vered (2021), the wanted recordings' folders need to be ordered in a manner matching the attached .xls file (from Hava's lab or segmentation algorithm), and the code creating the statistics. The recordings must be ordered:

• main folder containing all files and folders (can use E: or create new)
• sub-folders named after the mother, matching the .xls file
• sub-folders named after the cub, matching the .xls file
• matching recordings according to each cub's files
  (use these examples: 'total_data_recordings', 'data_united_recordings')