diff --git a/.gitignore b/.gitignore index b6e4761..c8005b5 100644 --- a/.gitignore +++ b/.gitignore @@ -1,105 +1,5 @@ -# Byte-compiled / optimized / DLL files +# Python Cache __pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -pip-wheel-metadata/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -.python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py # Environments .env @@ -109,21 +9,3 @@ venv/ ENV/ env.bak/ venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ diff --git a/LICENSE b/LICENSE index 4a5ebe8..a54dd2b 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2021 Miles Thorogood +Copyright (c) 2022 Miles Thorogood, Joshua Kranabetter Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/README.md b/README.md index bcb8c88..c2c2acf 100644 --- a/README.md +++ b/README.md @@ -1,38 +1,43 @@ -# BFsegmenter +# BFSegmenter -## Requirements: +The BFSegmenter segments audio files and classifies each segment as background, foreground, or background with foreground. Additionaly, for each segment the affect is predicted on a scale of valence and arousal. -sklearn +![Pipeline](/images/pipeline.png) -numpy +Sound designers and soundscape composers manually segment audio files into building blocks for use in a composition. **Machine learning (ridge regression)** is used to classify segments in an audio file automatically. The model has a **83.0% true positive classification rate**. -matplotlib +Russel’s model +suggests all emotions are distributed in a circular space (https://psycnet.apa.org/record/1981-25062-001). +High levels of valence correspond to pleasant sounds while +low valence levels correspond to unpleasant sounds. Further, high levels of arousal correspond to exciting sounds while low levels correspond to calming sounds. **Levels of valence and arousal are quantified using machine learning for emotion prediction (random forest regression)**. The emotion prediction models use a subset of extracted features to predict valence and arousal on a scale from -1 to 1 for each segment in an audio file. -sqlite3 +![Affect Accuracy](/images/affect_accuracy.png) -Python 2.7 (porting to Python 3 is a task) +Example implimentation of the segmenter in *extract_audacity_labels.py*. -YAAFE https://github.com/Yaafe/Yaafe +## Segment format -pydub https://github.com/jiaaro/pydub + bf type + duration + start + end + features + arousal + valence + bf probabilities -## TODO: +## Dependancies +Essentia - an open-source library for tools for audio and music analysis, description and synthesis. https://essentia.upf.edu/ -port to Python 3 +Scikit-learn - a free software machine learning library for the Python programming language. -corpusAuquireDir.py +For full requirements, check *requirements.txt*. - svnsegmenter.py +## Authors - bf_classifier.py + - Miles Thorogood + - Joshua Kranabetter - affect_predictor.py +## License - yaafeEngine.py - -## Running -python corpusAquireDir.py /path/to/audio/files - -audio files are aif or wav - -segments the files in the directory and puts the segs into SegmentedCorpus +This project is licensed under the MIT License - see the *LICENSE* file for details. diff --git a/affect_predictor.py b/affect_predictor.py index 32b088d..8f9932c 100644 --- a/affect_predictor.py +++ b/affect_predictor.py @@ -1,66 +1,51 @@ - -#!/usr/bin/env python - import numpy as np -import csv -import matplotlib.pyplot as plt -from sklearn import linear_model -import math +from sklearn.ensemble import RandomForestRegressor class AffectPredict: - """docstring for AffectPredict""" - + ''' + Emotion prediction models for valence and arousal. + ''' def __init__(self): + # masks to select features + self.AROUSAL_MASK = [11, 27, 29, 30, 33, 34, 48, 80, 89, 98, 110, 117, 118, 127, 128, 131, 133, 146, 166, 171, 203, 204, 219, 221, 236, 239, 261, 262, 264, 266, 267, 268, 343, 346, 347, 348, 349, 354, 355, 356, 364, 377, 379, 382, 383, 397, 437, 448, 450, 455, 463, 467, 468, 475, 478, 485, 487, 488, 491, 494, 497, 498, 508, 512, 518, 527, 529, 530, 531, 537, 539, 541, 544, 550, 557, 560, 573, 576, 580, 583, 584, 588, 596, 599, 602] + self.VALENCE_MASK = [0, 12, 27, 29, 31, 32, 33, 35, 37, 39, 42, 45, 46, 48, 49, 53, 55, 75, 79, 86, 87, 89, 91, 93, 95, 104, 106, 118, 127, 131, 133, 134, 135, 138, 140, 148, 152, 155, 156, 157, 158, 159, 161, 166, 175, 180, 182, 183, 185, 186, 187, 188, 189, 195, 197, 200, 206, 207, 216, 218, 221, 227, 231, 242, 245, 248, 249, 254, 255, 256, 257, 264, 269, 270, 334, 335, 337, 339, 341, 353, 354, 357, 360, 364, 366, 367, 369, 370, 371, 373, 374, 376, 377, 378, 380, 381, 382, 384, 388, 389, 391, 393, 395, 398, 411, 412, 415, 416, 417, 418, 419, 423, 424, 425, 431, 432, 433, 435, 436, 437, 438, 443, 445, 449, 451, 458, 461, 462, 466, 468, 469, 470, 471, 473, 474, 475, 481, 482, 483, 485, 488, 489, 496, 497, 498, 499, 501, 505, 517, 518, 521, 523, 526, 528, 529, 530, 534, 535, 537, 539, 540, 542, 543, 544, 545, 549, 550, 553, 554, 555, 560, 563, 568, 573, 575, 577, 580, 583, 584, 585, 586, 599, 602] - f = open('studyData_.csv','rb') - - self.header = f.readline().split(',') - training_data = np.loadtxt(f,delimiter=",",skiprows=1) - - self.Y = training_data[:,0:2] # first two colums are valence and arousal - self.X = training_data[:,2:len(training_data)] # remaining columns are audio features - - assert len(self.Y) == len(self.X) - - self.valence_model = linear_model.LinearRegression() # Ridge (alpha = .5) - self.valence_model.fit(self.X,self.Y[:,0]) - - self.arousal_model = linear_model.LinearRegression() - self.arousal_model.fit(self.X,self.Y[:,1]) - - + fa = open('datasets/arousal_data.csv','r') + fv = open('datasets/valence_data.csv','r') + + self.arousal_header = fa.readline().split(',') + self.valence_header = fv.readline().split(',') + + arousal_data = np.loadtxt(fa,delimiter=",") + valence_data = np.loadtxt(fv,delimiter=",") + + self.arousal_y = arousal_data[:,-1:] + self.arousal_X = arousal_data[:,0:-1] + self.valence_y = valence_data[:,-1:] + self.valence_X = valence_data[:,0:-1] + + # verify correct training data length + assert len(self.arousal_X) == len(self.arousal_y) + assert len(self.valence_X) == len(self.valence_y) + + # apply mask to get select features only + self.arousal_X = [x[self.AROUSAL_MASK] for x in self.arousal_X] + self.valence_X = [x[self.VALENCE_MASK] for x in self.valence_X] + + # create arousal model + self.arousal_model = RandomForestRegressor(max_depth=20, min_samples_split=5, oob_score=True) + self.arousal_model.fit(self.arousal_X, self.arousal_y.ravel()) + + # create valence model + self.valence_model = RandomForestRegressor(max_depth=30, min_samples_leaf=2, min_samples_split=5, oob_score=True) + self.valence_model.fit(self.valence_X, self.valence_y.ravel()) + def predict_valence(self, Z): - - return self.valence_model.predict(Z) - - def predict_arousal(self, Z): - - return self.arousal_model.predict(Z) - - def model_stats(self): + return self.valence_model.predict([Z]).item(0) - print("Valence RSS: %.2f" - % np.mean((self.valence_model.predict(self.X) - self.Y[:,0]) ** 2)) - print("Arousal RSS: %.2f" - % np.mean((self.arousal_model.predict(self.X) - self.Y[:,1]) ** 2)) - print('Valence variance score: %.2f' % self.valence_model.score(self.X, self.Y[:,0])) - print('Arousal variance score: %.2f' % self.arousal_model.score(self.X, self.Y[:,1])) - - def visualize_model(self, x, y, m, c): - ''' - TODO: needs finxin - ''' - w = 2 - h = 2#math.floor(len(y)/2) - f, axarr = plt.subplots(w,h) - count = 0 - for i in range(w): - for j in range(h): - axarr[i,j].plot(x[:,count], y, 'o', markersize=3) # label='Original data' - axarr[i,j].plot(x[:,count], m[count]*x[:,count]+c, 'r') # label='Fitted line' - axarr[i,j].set_title(self.header[2+count]) - #axarr[i,j].set_yscale('exp') - count += 1 + def predict_arousal(self, Z): + return self.arousal_model.predict([Z]).item(0) - plt.legend() - plt.show() \ No newline at end of file + def model_stats(self): + print('arousal r-squared score: %.2f' % self.arousal_model.oob_score_) + print('valence r-squared score: %.2f' % self.valence_model.oob_score_) \ No newline at end of file diff --git a/affect_studyData_all.csv b/affect_studyData_all.csv deleted file mode 100644 index df7e214..0000000 --- a/affect_studyData_all.csv +++ /dev/null @@ -1,568 +0,0 @@ -"xResponse","yResponse","PerceptualSharpness_1_mean","PerceptualSharpness_1_std","Loudness_1_mean","MFCC_2_std","ZCR_1_std","MFCC_1_std","Loudness_1_std","MFCC_2_mean","Energy_1_std","MFCC_3_mean","ZCR_1_mean","SpectralVariation_1_mean","Energy_1_mean","SpectralVariation_1_std","MFCC_3_std","MFCC_1_mean" 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import RidgeClassifierCV class BFClassifier(object): """ The famous classier for segmenting audio files into background, - foreground, and background with foreground segments + foreground, and background with foreground segments. """ + def __init__(self): - super(BFClassifier, self).__init__() - - # load the svm format training data - #