Given one or more time series of the same source, find tones that are significantly above background noise. Multi-taper harmonic analysis is used to identify time-frequency pixels containing signal and machine vision techniques are used to turn adjacent pixels into contours. Output are bounding boxes consisting of start and stop times, and low and high frequencies. Designed for audio recordings of behaving animals but should generalize.
A recent version of Matlab (at least 2012a), plus the image processing, signal processing, and statistics toolboxes. Groundtruthing also requires the mapping toolbox.
A computer with a lot cores is highly recommended, or better yet, a small cluster. The former requires the parallel computing toolbox, and the latter additionally requires the distributed computing server or the matlab compiler.
Alternatively, ax1(), the computational bottleneck, has been ported to both Julia and Python. Both of these languages are open source (i.e. free) and can access a cluster without compilation or use of the unix command line.
First, create a text file containing your parameters of choice:
FS=450450;
NFFT=32;
NW=3;
K=5;
PVAL=0.01;
frequency_low=20e3;
frequency_high=120e3;
convolution_size=[1300, 0.001];
minimum_object_area=18.75;
merge_harmonics=0;
merge_harmonics_overlap=0.9;
merge_harmonics_ratio=0.1;
merge_harmonics_fraction=0.9;
minimum_vocalization_length=0;
channels=[];
Then, given a file named rawdata.wav which contains N time series, use ax1() to generate rawdata-0.ax, which contains a sparse matrix of significant time-frequency pixels:
>>ax1('parameters.txt', 'rawdata.wav', '0')
Finally, use ax2() to generate rawdata.voc, which contains a list of bounding boxes:
>>ax2('parameters.txt', {'rawdata-0.ax'}, 'rawdata')
Parameters can also be directly specified as input arguments:
>>ax1(450450, 32, 3, 5, 0.01, 'rawdata.wav', '1')
>>ax2(20e3, 120e3, [1300, 0.001], 18.75, 0, 0.9, 0.1, 0.9, 0,...
{'rawdata-0.ax'}, 'rawdata')
Time-frequency pixels can be calculated from the same raw data using multiple different sets of parameters. The case below creates rawdata-0.ax, rawdata-1.ax, and rawdata-2.ax, each of which uses a different value for NFFT.
>>ax1(450450, 32, 3, 5, 0.01, 'rawdata.wav', '0')
>>ax1(450450, 64, 6, 11, 0.01, 'rawdata.wav', '1')
>>ax1(450450, 128, 11, 21, 0.01, 'rawdata.wav', '2')
Pixels from all .ax files with the specified base file name are combined by ax2() to create a single list of bounding boxes:
>>ax2(20e3, 120e3, [1300, 0.001], 18.75, 0, 0.9, 0.1, 0.9, 0, ...
{'rawdata-0.ax','rawdata-1.ax','rawdata-2.ax'}, 'rawdata')
To test the accuracy first manually annotate the raw data (with e.g. Tempo) to create human.voc, and then use groundtruth() to compute Ax's false alarm and miss rate:
>>[miss, false_alarm]=groundtruth('human.voc', 'rawdata.voc', [])
The parameter space can be searched for the set which minimizes errors using the script in optimize_parameters.m (forthcoming).
Jobs can be batched to a cluster of computers which uses the Sun Grid Engine scheduler as follows:
%./compile.sh # this only needs to be done once
%./cluster.sh ./parameters.txt ./rawdata.wav 3
Note that to process .bin files generated by Omnivore, compile.sh needs to be edited to specify the full path to binread.m.
Whereas the text file can only contain a single set of parameters when used with ax1(), it can contain multiple sets with cluster.sh:
FS=450450;
NFFT=[128, 64, 32];
NW=[11, 6, 3];
K=[21, 11, 5];
PVAL=0.01;
For more details, including file formats, see the documentation at the top of each file of source code.
Ben Arthur, arthurb@hhmi.org
Scientific Computing
Janelia Farm Research Campus
Howard Hughes Medical Institute