automatic import of python-amfm-decompy

3 files changed, 101 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..e70d166 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/AMFM_decompy-1.0.11.tar.gz
diff --git a/python-amfm-decompy.spec b/python-amfm-decompy.spec
new file mode 100644
index 0000000..9e59d8f
--- /dev/null
+++ b/python-amfm-decompy.spec
@@ -0,0 +1,99 @@
+%global _empty_manifest_terminate_build 0
+Name:		python-AMFM-decompy
+Version:	1.0.11
+Release:	1
+Summary:	Package containing the tools necessary for decomposing a speech signal into its modulated components, aka AM-FM decomposition.
+License:	LICENSE.txt
+URL:		https://github.com/bjbschmitt/AMFM_decompy/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/e7/ea/9364ece82c511c85131e24012dcf37d2aca876db7ea18799e8969fc5e8b6/AMFM_decompy-1.0.11.tar.gz
+BuildArch:	noarch
+
+
+%description
+version 1.0.11
+This python package provides the tools necessary for decomposing the voiced part of a speech signal into its modulated components, aka AM-FM decomposition. This designation is used due the fact that, in this method, the signal is modeled as a sum of amplitude- and frequency-modulated components.
+The goal is to overcome the drawbacks from Fourier-alike techniques, e.g. SFFT, wavelets, etc, which are limited in the time-frequency analysis by the so-called Heisenberg-Gabor inequality.
+The algorithms here implemented are the QHM (Quasi-Harmonic Model), and its upgrades, aQHM (adaptive Quasi-Harmonic Model) and eaQHM (extended adaptive Quasi-Harmonic Model). Their formulation can be found at references [2-4].
+Since that the tools mentioned above require a fundamental frequency reference, the package also includes the pitch tracker YAAPT (Yet Another Algorithm for Pitch Tracking) [1], which is extremely robust for both high quality and telephone speech.
+The study of AM-FM decomposition algorithms was the theme from my Master Thesis. The original YAAPT program in MATLAB is provided for free by its authors, while the QHM algorithms I implemented by myself also in MATLAB. I'm porting them now to python because:
+* the python language is easier to share, read and understand, making it a  better way to distribute the codes;
+* is more resourceful than MATLAB (has different data structures, scripting  options, etc), which will be useful for me in future studies;
+* the computational performance from its numeric and scientific packages (numpy  and scipy) is equivalent to MATLAB;
+* python is free-to-use, while MATLAB is a proprietary software;
+
+%package -n python3-AMFM-decompy
+Summary:	Package containing the tools necessary for decomposing a speech signal into its modulated components, aka AM-FM decomposition.
+Provides:	python-AMFM-decompy
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-AMFM-decompy
+version 1.0.11
+This python package provides the tools necessary for decomposing the voiced part of a speech signal into its modulated components, aka AM-FM decomposition. This designation is used due the fact that, in this method, the signal is modeled as a sum of amplitude- and frequency-modulated components.
+The goal is to overcome the drawbacks from Fourier-alike techniques, e.g. SFFT, wavelets, etc, which are limited in the time-frequency analysis by the so-called Heisenberg-Gabor inequality.
+The algorithms here implemented are the QHM (Quasi-Harmonic Model), and its upgrades, aQHM (adaptive Quasi-Harmonic Model) and eaQHM (extended adaptive Quasi-Harmonic Model). Their formulation can be found at references [2-4].
+Since that the tools mentioned above require a fundamental frequency reference, the package also includes the pitch tracker YAAPT (Yet Another Algorithm for Pitch Tracking) [1], which is extremely robust for both high quality and telephone speech.
+The study of AM-FM decomposition algorithms was the theme from my Master Thesis. The original YAAPT program in MATLAB is provided for free by its authors, while the QHM algorithms I implemented by myself also in MATLAB. I'm porting them now to python because:
+* the python language is easier to share, read and understand, making it a  better way to distribute the codes;
+* is more resourceful than MATLAB (has different data structures, scripting  options, etc), which will be useful for me in future studies;
+* the computational performance from its numeric and scientific packages (numpy  and scipy) is equivalent to MATLAB;
+* python is free-to-use, while MATLAB is a proprietary software;
+
+%package help
+Summary:	Development documents and examples for AMFM-decompy
+Provides:	python3-AMFM-decompy-doc
+%description help
+version 1.0.11
+This python package provides the tools necessary for decomposing the voiced part of a speech signal into its modulated components, aka AM-FM decomposition. This designation is used due the fact that, in this method, the signal is modeled as a sum of amplitude- and frequency-modulated components.
+The goal is to overcome the drawbacks from Fourier-alike techniques, e.g. SFFT, wavelets, etc, which are limited in the time-frequency analysis by the so-called Heisenberg-Gabor inequality.
+The algorithms here implemented are the QHM (Quasi-Harmonic Model), and its upgrades, aQHM (adaptive Quasi-Harmonic Model) and eaQHM (extended adaptive Quasi-Harmonic Model). Their formulation can be found at references [2-4].
+Since that the tools mentioned above require a fundamental frequency reference, the package also includes the pitch tracker YAAPT (Yet Another Algorithm for Pitch Tracking) [1], which is extremely robust for both high quality and telephone speech.
+The study of AM-FM decomposition algorithms was the theme from my Master Thesis. The original YAAPT program in MATLAB is provided for free by its authors, while the QHM algorithms I implemented by myself also in MATLAB. I'm porting them now to python because:
+* the python language is easier to share, read and understand, making it a  better way to distribute the codes;
+* is more resourceful than MATLAB (has different data structures, scripting  options, etc), which will be useful for me in future studies;
+* the computational performance from its numeric and scientific packages (numpy  and scipy) is equivalent to MATLAB;
+* python is free-to-use, while MATLAB is a proprietary software;
+
+%prep
+%autosetup -n AMFM-decompy-1.0.11
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-AMFM-decompy -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.11-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..6dd0ce2
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+0cffe71f05b8fd69807ffb73226dbe68  AMFM_decompy-1.0.11.tar.gz