From: Dominique Fourer <dfourer@dfourer-ThinkPad-T430.(none)>
Date: Sat, 13 Dec 2014 18:36:26 +0000 (+0100)
Subject: New Plugin for multiple F0 estimation (wrapped from C++ code, see fmultipitch.so)
X-Git-Url: https://git.parisson.com/?a=commitdiff_plain;h=cb412ffee461a6586d8f9d584a584d3acc0d8478;p=timeside.git

New Plugin for multiple F0 estimation (wrapped from C++ code, see fmultipitch.so)
---

diff --git a/timeside/analyzer/labri/fmultipitch.so b/timeside/analyzer/labri/fmultipitch.so
new file mode 100755
index 0000000..f9765b1
Binary files /dev/null and b/timeside/analyzer/labri/fmultipitch.so differ
diff --git a/timeside/analyzer/labri_instru.py b/timeside/analyzer/labri_instru.py
index 2a943d5..9e768fa 100644
--- a/timeside/analyzer/labri_instru.py
+++ b/timeside/analyzer/labri_instru.py
@@ -31,7 +31,7 @@ from timeside.analyzer.preprocessors import frames_adapter
 ## plugin specific
 import sys, os
 REL_PATH='labri';
-PLUGIN_PATH=os.path.join(timeside.__path__[0], 'REL_PATH');
+PLUGIN_PATH=os.path.join(timeside.__path__[0], REL_PATH);
 sys.path.append(PLUGIN_PATH);
 sys.path.append(REL_PATH);		## can be commented
 import timbre_descriptor
diff --git a/timeside/analyzer/labri_multipitch.py b/timeside/analyzer/labri_multipitch.py
new file mode 100644
index 0000000..e1e52ae
--- /dev/null
+++ b/timeside/analyzer/labri_multipitch.py
@@ -0,0 +1,115 @@
+# -*- coding: utf-8 -*-
+#
+# labri_multipitch.py
+#
+# Copyright (c) 2014 Dominique Fourer <dominique@fourer.fr>
+
+# This file is part of TimeSide.
+
+# TimeSide is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+
+# TimeSide is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License
+# along with TimeSide.  If not, see <http://www.gnu.org/licenses/>.
+
+# Author: D Fourer <dominique@fourer.fr> http://www.fourer.fr
+from __future__ import absolute_import
+from timeside.analyzer.utils import segmentFromValues
+from timeside.core import Processor, implements, interfacedoc, FixedSizeInputAdapter
+from timeside.analyzer.core import *
+from timeside.api import IAnalyzer
+import timeside
+
+
+## plugin specific
+import sys, os
+REL_PATH='labri';
+PLUGIN_PATH=os.path.join(timeside.__path__[0], REL_PATH);
+sys.path.append(PLUGIN_PATH);
+sys.path.append(REL_PATH);		## can be commented
+import fmultipitch
+import numpy, scipy
+from timeside.analyzer.preprocessors import frames_adapter
+
+class LABRIMultipitch(Analyzer):
+	"""
+	Labri Multipitch Estimation
+	this module performs multiple F0 estimation using sinusoidal
+	models based on spectral reassignment and adapted harmonic source
+	model.
+	Details are in:
+	[D. Fourer. (in french) PhD thesis. Informed approach applied 
+	sound and music analysis (in french), Dec 2013]
+	[D. Fourer and S. Marchand. Informed Multiple-F0 Estimation 
+	Applied to Monaural Audio Source Separation. Proc. EUSIPCO'12,
+	 Bucharest, Romania, August 2012]
+	"""
+	implements(IAnalyzer)
+	
+	@interfacedoc
+	def setup(self, channels=None, samplerate=None, blocksize=None, totalframes=None):
+		super(LABRIMultipitch, self).setup(channels, samplerate, blocksize, totalframes)
+		self.Fs			= self.samplerate();
+		self.t_index 	= 0.;
+		self.result		= AnalyzerResultContainer();
+		self.values		= list();
+	@staticmethod
+	@interfacedoc
+	def id():
+		return "labri_multipitch"
+
+	@staticmethod
+	@interfacedoc
+	def name():
+		return "Labri Multiple F0 estimation"
+
+	@staticmethod
+	@interfacedoc
+	def unit():
+		# return the unit of the data dB, St, ...
+		return "F0 vector in Hz"
+        
+	def __str__(self):
+		return "Labeled Instrument segments"
+	
+	def process(self, frames, eod=False):
+		
+		s,N = prepare_signal(frames);
+		self.t_index = self.t_index + float(N) / float(self.Fs);     ##time index
+		f0_candidates, t = fmultipitch.analysis(s, N, self.Fs);
+		self.values.append([numpy.unique(numpy.array(numpy.squeeze(f0_candidates))), self.t_index ]);   ## (F0_vector, time_t)
+		
+		return frames, eod;
+
+	def post_process(self):
+		result 					= self.new_result(data_mode='value', time_mode='framewise')
+		result.data_object.value= np.vstack(self.values)
+		
+		#self.add_result(result);    ## doesn't work !
+		self.result.add(result);
+
+## prepare data before analysis
+def prepare_signal(s):
+	s = numpy.array(numpy.squeeze(s), float);
+
+	## Stereo to mono
+	if s.ndim > 1:
+		sz = numpy.shape(s);
+		if sz[0] < sz[1]:
+			s = s.T;
+		s = numpy.sum(s, axis=0);
+	
+	sz = numpy.shape(s);
+	## normalize
+	N = len(s);	
+	#signal = s/max(abs(s)) * 1.;
+	return s, N;
+	
+