FLY SONG SEGMENTER A tool for analyzing audio recordings of the courtship song of the fruit fly Drosophila melanogaster and related species. Given the raw time series, the program outputs time stamps demarcating the beginning and end of both sine and pulse song. COPYRIGHT Copyright (c) 2013, Princeton University and Howard Hughes Medical Institute, All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of Princeton University and/or the Howard Hughes Medical Institute nor the names of its contributors (Mala Murthy and/or David Stern) may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, ANY IMPLIED WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT, OR FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; REASONABLE ROYALTIES; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. SYSTEM REQUIREMENTS Matlab and the following toolboxes: Signal, Statistics, Wavelet, and not-necessary but highly-recommended Distributed Computing. RUNNING ON YOUR LOCAL MACHINE Start Matlab and use FlySongSegmenter() to analyze data already loaded into the Matlab workspace or FlySongSegmenterDAQ() to analyze .daq files on disk without first loading them. The former returns the results to the workspace, while the latter saves them to disk in a .mat file. Either way, the results can be viewed with PlotSegmentation(). All parameters are contained in FetchParams.m. MORE DETAILS 1mintestsong.mat contains three test songs (easy, medium, difficult) and one sample of noise. Load it into your workspace: >> load 1mintestsong You can segment song with or without the noise file. If you don't provide a noise file, then noise is estimated from the data. To segment the song: >> [data,Sines,Pulses,Params] = FlySongSegmenter(easy,[],[]) To view the results: >> PlotSegmentation(data,Sines,Pulses) Sines is a structure that has all of the information on the sine song and Pulses is a structure that has all of the information on the pulses. You can then analyze these extracted elements (individual pulses and sinusoids) however you like. Specifically, Sines.TimeHarmonicMerge contains sine song which has been merged over continguous time segments and across harmonics; Sines.PulsesCull is the subset of Sines.TimeHarmonicMerge which does not occur within pulse song; and Sines.LengthCull is the subset of Sines.PulsesCull which exceeds a specified minimum length. Similarly, Pulses.AmpCull is the subset of Pulses.Wavelet which exceeds a specified amplitude, and Pulses.IPICull is the subset of Pulses.AmpCull which is within specified bounds on IPI and fundamental frequency. Pulses.ModelCull and Pulses.ModelCull2 are subsets of Pulses.AmpCull and Pulses.IPICull, respectively, which fit a specified model. To optimize segmentation for your recordings, you might have to adjust several parameters by modifiying params.m. We suggest you do this on a small (~1 min) but representative section of your data. The utility FlySongSegmenterByHand() provides a way to manually ground truth data, and CompareManual2AutoSegmentation() calculates how well manual and automatic segmentation coincide. There is much more information about how FlySongSegmenter works buried in the code of its subfunctions -- please read all of the comments. LIGHTSPEED TOOLBOX Depending on your machine architecture, you might see dramatic speed improvements with: http://research.microsoft.com/en-us/um/people/minka/software/lightspeed/ FORKS For a machine learning pulse classifier, and an updated manual segmenter, see https://github.com/murthylab/songSegmenter