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znote consists of a suite of libraries and programs for spectrotemporal decomposition of acoustic signals. Many bioacoustic signals, such as birdsong, consist of a set of spectrotemporally disjoint "objects". These objects may overlap in time with each other, as when a bird is vocalizing with both sides of its syrinx simultaneously; or overlap with sounds from other animals or the environment, as in field recordings. It is often possible to separate these objects from each other in the spectrotemporal domain, but it then becomes necessary to reconstruct the sound pressure waveforms giving rise to the isolated spectrotemporal components.

The first stage is to identify the components of the vocalization. znote_label provides one method of doing this, by finding all the connected components in a signal. Briefly, the spectrogram of the signal is computed, and all the points which are above this spectrogram are grouped into contiguous features. The output is an array with the same dimensions of the spectrogram, in which each time-frequency point is given an integer code indicating which component it belongs to.

The second stage is to invert the spectrographic transform for each of the identified components. This is done by znote_extract, which takes as input the original signal and the array indicating which points in the spectrogram belong to which features. It is not necessary to use znote_label to generate this array, and the feature-file can be manipulated after it is generated to group or split components. zedit, a MATLAB GUI for simple feature manipulation is provided.

For more information on the algorithm, see:

Meliza CD, Chi Z, Margoliash D, "Representations of Conspecific Song by Starling Secondary Forebrain Auditory Neurons: Towards a Hierarchical Framework". J Neurophysiology, doi:10.1152/jn.00464.2009

License and warranty

Use of the code is free for non-commercial purposes under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License (

C Daniel Meliza, Zhiyi Chi, and Daniel Margoliash (or the above paper) will be acknowledged as the source of the algorithms in any publications reporting its use or the use of any modified version of the program.


To contact the authors, please consult the official repository for znote at

Compilation and Installation


znote_label and znote_extract require

zedit requires a reasonably recent version of MATLAB

Mac OS X

Znote was developed on OS X 10.6 (Snow Leopard). OS X comes with LAPACK as part of the vecLib framework. FFTW, blitz, and libsndfile can be installed through the macports system, as follows:

sudo port install blitz fftw-3 libsndfile

Scons is also available through macports, although this will cause a separate copy of python to be installed under the macports tree, if it is not already.

sudo port install scons

The SConstruct file, as supplied, should compile the programs on any system with XCode installed:

scons -Q -j2


Znote has been compiled and tested on an x86_64 Opteron system running Redhat EL5, with the ATLAS BLAS libraries and NETLIB LAPACK libraries installed under /usr/local. If necessary, edit the lib_path and include_path in SConstruct to point to directories where the appropriate headers and libraries are located.


Znote has been successfully compiled on windows XP using the MinGW compiler (i.e. gcc 3.4). Getting all the dependencies compiled is tricky. Blitz (0.9) does not compile, or work with gcc 4.4. I recommend installing the enthought Python distribution, which ships with MinGW, and installing Msys or Cygwin in order to run the configure scripts for the dependencies. FFTW3, blitz and libsndfile should compile out of the box. Use static libraries if possible to avoid having dependencies on DLLs. LAPACK needs to be compiled with g77, not gfortran. The SConstruct file assumes that the dependencies have been installed under the znote source tree.

Hopefully, you will not have to do any of this - znote_label.exe and znote_extract.exe were compiled on Windows XP. Like the rest of znote, no support is offered.

Performance notes

FFTW can be configured to use multiple threads, which can offer some speed improvement for large sound files. For small sound files, the overhead of setting up multiple threads is rarely worth it. To enable multiple threads in znote, edit SConstruct and set threads to some number less than or equal to the number of cores in your system.


An example is provided under test/, consisting of a recorded starling vocalization (A8.wav) and labeled components of the vocalizations (A8_feats.bin). To test your install, run

znote_label --pad --recon test/A8.wav test/A8_feats.bin

This will generate a separate file for each component of the signal, and a reconstruction formed by adding all the components together. Compare A8_recon.wav with A8_recon_reference.wav.


znote_label [--nfft <i>] [--fftshift <i>]  [--ntapers <i>] [--nw <f>]
                [--thresh <f>] [--df <f>] [--dt <f>]
                [--min-size <f>] <input>

nfft: controls the size of the FFT analysis window. Default 512, which is appropriate most signals sampled at around 44 kHz. Larger values give higher frequency resolution at the expense of lower temporal resolution. The value of nfft is most important at this stage, because it determines the time-frequency resolution of algorithm that detects connected components.

fftshift: controls the spacing between FFT analysis windows. Default is 10, which gives a substantial amount of overlap between frames. Increasing the value can increase the speed of the algorithm, at some cost to the temporal resolution during labelling.

nw: this program uses a multitaper algorithm to estimate spectral density. Increasing the time-bandwidth product increases thes stability of these estimates, but at the expense of lower spectral resolution. The default value of 3.5 gives a decent amount of smoothing. Larger values give more smoothing, but neighboring components may get smeared together. Smaller values can improve resolution between neighboring components, but tend to underestimate the ST extent of the components and increase the number of points where the power goes above threshold spuriously. Needs to be a half-integer (i.e. 3,3.5,4,...)

ntapers: provides further control over spectrogram estimation. Defaults to nw/2-1, which is generally considered to be the optimal value.

thresh: set the minimum power for a component. This can be specified in absolute terms, in dB, or relative to the total amount of power in the signal. If the value is greater than 1.0, the threshold is calculated as an absolute value, and only the points in the spectrogram where the power is greater than this value are considered to be "above water" for the detection of components. If less than 1.0, the absolute threshold is calculated as the power corresponding to the quantile . Default is 0.5 (or 50%). Note that the relative threshold is calculated on a linear scale, so 50% of the power is often confined to a fairly small portion of the signal.

df: control frequency resolution of component search algorithm. Components are considered to be connected if they are less than df Hz apart. Defaults to 200 Hz. Along with dt, increasing values lead to fewer, larger components.

dt: control temporal resolution of component search algorithm. Defaults to 2 ms.

min-size: Components with less than kHz-ms area are dropped.

The input file to znote_label can be a sound file (in any format libsndfile understands), or a .bin file containing the spectrogram of the signal. Consult blitz_io.hh for documentation on the .bin format. The behaviors of many of the flags change when using a pre-calculated spectrogram, so this is not recommended for novice users.

The program outputs a .bin file indicating which points in the spectrogram belong to which features.


znote_extract [--fbdw <f>] [--tbdw <f>]
               [--feat <i>] [--pad] [--del] [--recon]
               SIGNAL LABELFILE

znote_extract uses the labels defined in LABELFILE to generate masks, which it uses to extract the associated time series in SIGNAL. The masks are generated with a Gaussian roll-off filter, the parameters of which are controlled on the command line:

fbdw: Set frequency bandwidth for Gaussian roll-off mask. Defaults to 200 Hz. Larger values reduce edge effects, but at the cost of potentially interfering with neighboring components, or including more noise.

tbdw: Set time bandwidth for smoothing kernel. Defaults to 2 ms.

feat: By default, the program extracts all the component defined in ; set this value to a nonnegative integer to restrict to a single component.

pad: By default, the program generates unpadded output files; if this flag is set, then the output signals are the same length as the input signal, with all points where the component was not present set to 0.

del: If set, the program will also generate deletions, which are calculated by substracting (at the appropriate temporal offset) the extracted components from the original signal.

recon: If set, the program will sum all the extracted components at their original offset and output the resulting sum.

SIGNAL must be a sound file, because the program needs the original phase information to reconstruct the signals.

LABELFILE can be any integer bin file, including the file output by znote_label. The dimensions of the file will be used to control the FFT parameters of the extraction algorithm.


znote_extract writes one wave file for each extracted component. If the input file is named signal.wav, the output files will be named signal_feature000.wav, signal_feature001.wav, etc.

For component deletions, the output files are named as signal_fdel000.wav, etc

The reconstruction has the name signal_recon.wav


zedit is a simple MATLAB interface for editing .bin files. It allows merging and splitting of components while visualizing the spectrogram of the corresponding signal. To edit components for a signal, run zedit in MATLAB as follows:

>> zedit <wavefile>

zedit runs znote_label to generate spectrograms and calculate connected components. If the executable is not in your path, you may need to edit zedit_params.m When the program first runs, it will calculate the spectrogram of <wavefile> and display it with a single contour indicating where the threshold lies.

The parameters of the spectrographic transform can be changed in the FFT/MTM panel. The threshold value can be edited manually or by clicking on the colorbar to the right of the spectrogram.

In the LabelSet panel, to calculate components, click the Label button. Note: this will overwrite the file <wavefile>_labels.bin. To load a previously generated label file, click Load.

When a labelset is selected, a list of features is displayed in the Features panel. Selecting one or more features causes them to be displayed in the spectrogram. Features can be merged with the Merge button, or split by clicking the Lasso button. After clicking Lasso, click points on the spectrogram to define a polygon around the feature of interest. Click the middle mouse button to close the polygon and split the feature. Only currently selected features are affected.

Save the edited labelset by clicking Save in the LabelSet panel. Choose a name for the output file; this can be used with znote_extract to generate the signals associated with the components.

Best practices

The algorithm in znote_extract can generate artifacts near edges of signals. If this is a problem, be sure to pad your signals with silence on either end.

Another source of error is when components overlap. Overlap is caused by the rolloff filter, so if your components are too close together, try decreasing these values. You can tell if overlap is occurring when znote_extract outputs a line like "Max feature overlap: 1.90596". If this value is 1.0 there is no overlap.

Version History


First public release.


Updated to compile with blitz 0.10. Should still compile with blitz 0.9. Fixed a bug where the mask was not being correctly applied to the row corresponding to half Nyquist.


Spectrotemporal decomposition of bioacoustic signals



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