Synthesis of Perceptually Plausible Multichannel Noise Signals Controlled by Real World Statistical Noise Properties
This Github repository provides code and basic information about the studies which were performed for the publication of the respective AES journal paper.
Head for the accompanying web site for more basic information about the study.
This MATLAB class provides means to analyze audio noise disturbances and synthesize artificial noise processes of arbitrary length with similar perceptive features.
The goal is to provide a tool for researchers and developers to generate artificial noise disturbances for evaluating algorithms in single and multi-channel applications.
The package has been tested under Windows and Linux with MATLAB versions >=
2014b 64bit. To use the class, make sure the folder containing the package
folder +NoiseSynthesis with all the class files is part of the MATLAB path.
Various methods to add a folder to MATLAB's path exist. To ensure the latter
for the current session, use
addpath(path_to_package);To make use of the mexfile rmsvec in the external folder of the package
(ie. +NoiseSynthesis/+external) it has to be compiled by calling
mex rmsvec.cppThe package has dependencies to the MATLAB Statistics and Machine Learning and Signal Processing toolbox.
In this part the basic and the more advanced functionalities of the class are
described. This README provides practical examples. For class documentation
refer to doc NoiseSynthesis.NoiseAnalysisSynthesis.
To instantiate a noise-synthesis object, simply call one of
obj = NoiseSynthesis.NoiseAnalysisSynthesis();
obj = NoiseSynthesis.NoiseAnalysisSynthesis(vSignal,fs);where vSignal is a signal vector of a noise process to be analyzed and fs
is the corresponding sampling rate in Hertz.
If the constructor is called without arguments the analysis method will fail. In this state the class can only synthesize a noise process based on one of the presets.
After creating a NoiseAnalysisSynthesis object as shown above the object
shows the following properties. The first properties describe the considered
signals like the analysis and synthesis (and optional click) signal. For model
parameters and error measures see below. The remaining properties describe some
general processing parameters as well as several Booleans steering which parts
of the process chain are desired.
>> obj
obj =
NoiseAnalysisSynthesis with properties:
AnalysisSignal: [358706x1 double]
SensorSignals: []
ClickTracks: {}
ModelParameters: [1x1 NoiseSynthesis.ModelParametersSet]
ErrorMeasures: [1x1 NoiseSynthesis.ErrorMeasures]
GammatoneLowestBand: 64
GammatoneHighestBand: 16000
NumModulationBands: 16
CutOffHP: 100
NumSensorSignals: 2
NumSources: 1
Fs: 44100
DesiredSignalLenSamples: 358706
bApplyColoration: 1
bApplyAmplitudeDistr: 1
bApplyModulations: 1
bApplyComodulation: 1
bApplySpatialCoherence: 1
bEstimateClickSpec: 1
bDeClick: 1The default, ie. empty, model parameters sub-object looks like the following (before the analysis step). Parameters which are not signal dependent are initialized with default values, the rest is empty.
>> obj.ModelParameters
NoiseSynthesis.ModelParametersSet with properties:
Model: 'manual'
ModulationFilterbank: 'mel'
ModulationDepth: [16x1 double]
ModulationSpeed: 'fast'
MarkovTransition: []
MarkovStateBoundaries: [10x2 double]
GammaBands: []
ClickTransition: []
fLowerClick: 2000
fUpperClick: 6000
SNRclick: Inf
MeanPSD: []
ColorNumOrd: 8
ColorDenumOrd: 8
AmplitudeModel: 'pareto'
Quantiles: []
CDF: []
CohereModel: 'cylindrical'
SensorPositions: [3x2 double]
SourcePosition: [3x1 double]
bApplyClicks: 0
ModulationWinLen: 0.0250There is also the possibility to view simple error measures between the analysis and synthesis signals if available.
>> obj.ErrorMeasures
ErrorMeasures with properties:
ColorationError: 0.1833
SpatialCoherenceError: 0.0016
ComodulationsError: 0.0037
AmplitudeDistributionError: 0.0035
ModulationError: 827.1058
AmplitudeErrorMethod: 'Bhattacharyya'
PsdErrorMethod: 'cosh'
ModulationErrorMethod: 'KullbackLeibler'
The following public methods are implemented (in alphabetical order):
>> methods(NoiseSynthesis.NoiseAnalysisSynthesis)
Methods for class NoiseSynthesis.NoiseAnalysisSynthesis:
NoiseAnalysisSynthesis % the constructor
analyze
flushParameters
playAnalyzed
playSynthesized
plot
readParameters
saveParameters
saveSignals
sound
soundsc
synthesizeTo start the analysis if a desired signal has been passed in the constructor
call the analyze method by
analyze(obj); % or equivalently
obj.analyze();When a complete model parameter set is present, ie. obj.ModelParameters has all
information, the synthesis method can be called by
synthesize(obj); % or equivalently
obj.synthesize();Now, the synthesized signal is available in obj.SensorSignals or it can be
directly retrieved by calling
SynthSignal = synthesize(obj); % or equivalently
SynthSignal = obj.synthesize();A complete example of the easiest way to use the analysis and synthesis methods is shown below.
% signal to be analyzed
[vSignal, fs] = audioread('noise.wav');
% create the object
obj = NoiseSynthesis.NoiseAnalysisSynthesis(vSignal,fs);
% call analysis and synthesize a two-channel signal
obj.analyze();
obj.synthesize();
% listen to results in comparison to analysis signal
sound(obj);
% plot some characteristics
plot(obj);It is possible to choose from a small set of presets for a synthesis. Supported presets for now are
- optical
- tape
- vinyl
- rain
- applause
and are saved as mat-Files in the package folder +NoiseSynthesis. The
following example shows the synthesis with presets.
% create the object without parameters
obj = NoiseSynthesis.NoiseAnalysisSynthesis();
% load parameters, for example vinyl
obj.loadParameters(fullfile('+NoiseSynthesis', 'vinyl'))
% synthesize a noise signal
obj.synthesize();
% listen to results in comparison to analysis signal
sound(obj);
% plot some characteristics
plot(obj)It is of course possible to save own presets when there is access to an
isolated noise files. To do so, follow the example by using the
saveParameters method.
% signal to be analyzed
[vSignal, fs] = audioread('noise.wav');
% create the object
obj = NoiseSynthesis.NoiseAnalysisSynthesis(vSignal,fs);
% call analysis
obj.analyze;
% now: save the parameters with desired file name
obj.saveParameters(fullfile('+NoiseSynthesis', 'myNoiseParameters'))Use the presets as shown in the previous example.
The software is published under BSD 3-Clause license.
Copyright (c) 2016, Jens-Alrik Adrian
Institute for Hearing Technology and Audiology
Jade University of Applied Sciences
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 the copyright holder nor the names of its
contributors 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, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
HOLDER 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; 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.