From 3ae9943bdf913dfd671e97d75698f3b6474f2438 Mon Sep 17 00:00:00 2001 From: Christina Theodoridou Date: Tue, 15 Jan 2019 11:50:33 +0200 Subject: [PATCH] preprocessing and evaluation section added - needs fixes --- presentation/features_and_preprocessing.tex | 95 ++++++++++++-------- presentation/main.pdf | Bin 666795 -> 673121 bytes presentation/main.tex | 1 + presentation/models.tex | 48 +++++++--- 4 files changed, 97 insertions(+), 47 deletions(-) diff --git a/presentation/features_and_preprocessing.tex b/presentation/features_and_preprocessing.tex index c616d91..52bc4f8 100644 --- a/presentation/features_and_preprocessing.tex +++ b/presentation/features_and_preprocessing.tex @@ -1,12 +1,12 @@ -\section{Η υλοποίησή μας} +\section{Εργαλεία που χρησιμοποιήθηκαν} Η υλοποίησή αναπτύχθηκε σε γλώσσα Python και χρησιμοποιήθηκαν πληθώρα βιβλιοθηκών όπως η essentia για την εξαγωγή χαρακτηριστικών, η sklearn για την προεπεξεργασία δεδομένων -ενώ στην εκπαίδευση των μοντέλων χρησιμοποιήθηκε η numpy. Χρησιμοποιήθηκε επίσης η seaborn για την εξαγωγή διαγραμμάτων και την οπτικοποίηση των χαρακτηριστικών. Παράλληλα, σε συνδυασμό με όλες αυτές χρησιμοποιήθηκαν και άλλες βιβλιοθήκες όπως η pandas, η matplotlib, η multiprocessing, η οs, η pyaudio κα. Για την εκπαίδευση, δοκιμάστηκαν τα μοντέλα svm, decision trees, multilayer perceptron, Naive bayes και random forest, τα οποία θα αναπτυχθούν στα επόμενα κεφάλαια. Το dataset που χρησιμοποιήθηκε για την εκπαίδευση του μοντέλου είναι το προτινόμενο GTZAN dataset \footnote{http://opihi.cs.uvic.ca/sound/music\_speech.tar.gz}, το οποίο αποτελείται απο 120 αρχεία διάρκειας 30 δευτερολέπτων. Κάθε κλάση (μουσική/φωνή) αποτελείται από 60 αρχεία ενώ δεν υπάρχουν αρχεία που να περιέχουν και τις δύο κλάσεις. Όλα τα δείγματα είναι στα 22050 Hz, Mono 16-bit και σε αρχεία μορφής .wav. Τέλος, χρησιμοποιήθηκε 4-fold cross validation για την εκτίμηση της απόδοσης του μοντέλου. +ενώ στην εκπαίδευση των μοντέλων χρησιμοποιήθηκε η numpy. Χρησιμοποιήθηκε επίσης η seaborn για την εξαγωγή διαγραμμάτων και την οπτικοποίηση των χαρακτηριστικών. Παράλληλα, σε συνδυασμό με όλες αυτές χρησιμοποιήθηκαν και άλλες βιβλιοθήκες όπως η pandas, η matplotlib, η multiprocessing, η οs, η pyaudio κα. Για την εκπαίδευση, δοκιμάστηκαν τα μοντέλα svm, decision trees, multilayer perceptron, Naive bayes και random forest, τα οποία θα αναπτυχθούν στα επόμενα κεφάλαια. Το dataset που χρησιμοποιήθηκε για την εκπαίδευση του μοντέλου είναι το προτινόμενο GTZAN dataset \footnote{http://opihi.cs.uvic.ca/sound/music\_speech.tar.gz}, το οποίο αποτελείται απο 120 αρχεία διάρκειας 30 δευτερολέπτων. Κάθε κλάση (μουσική/φωνή) αποτελείται από 60 αρχεία ενώ δεν υπάρχουν αρχεία που να περιέχουν και τις δύο κλάσεις. Όλα τα δείγματα είναι στα 22050 Hz, Mono 16-bit και σε αρχεία μορφής .wav. -\section{Χαρακτηριστικά και προεπεξεργασία δεδομένων} +\section{Χαρακτηριστικά} Για την εξαγωγή των χαρακτηριστικών που φαίνονται στο από τα αρχεία ήχουν του σετ δεδομένων, αρχικά τμηματήσαμε κάθε σήμα αρχείου σε frames με μέγεθος 6144 δείγματα, το οποίο προέκυψε μετά από επαναλαμβανόμενες δοκιμές. - Έπειτα, τα frames αυτά, παραθυροποιήθηκαν με παράθυρο τύπου Hamming, ίσου μεγέθους. Στη συνέχεια, έγινε η εξαγωγή των χαρακτηριστικών στο πεδίο του χρόνου, καθώς και στο πεδίο της συχνότητας.Παράλληλα, έγινε και η εξαγωγή των συντελεστών MFCC έτσι ώστε να είναι εφικτός ο υπολογισμός της διαμόρφωσης της ενέργειας σήματος στα 4 Ηz. Τα χαρακτηριστικά που εξήχθηκαν είναι 27 και θα αναλυθούν στη συνέχεια. Ως προεπεξεργασία, τα δεδομένα κλιμακοποιήθηκαν έτσι ώστε να έχουν μέσο 0 και τυπική απόκλιση ίση με 1 για κάθε ένα. Τέλος, δίνεται η δυνατότητα στον κώδικά να εφαρμοστεί και η μέθοδος Principal Component Analysis - PCA,αν αυτή είναι αναγκαία, έτσι ώστε να μειωθούν οι τελικές διαστάσεις των χαρακτηριστικών. Στην συγκεκριμένη υλοποίηση, μειώνονται οι διαστάσεις από 27 σε 10, έτσι ώστε η διαφορά να είναι αξιοσημείωτη και να ελεγθεί τι γίνεται σε αυτήν την περίπτωση με την απόδοση. + Έπειτα, τα frames αυτά, παραθυροποιήθηκαν με παράθυρο τύπου Hamming, ίσου μεγέθους. Στη συνέχεια, έγινε η εξαγωγή των χαρακτηριστικών στο πεδίο του χρόνου, καθώς και στο πεδίο της συχνότητας.Παράλληλα, έγινε και η εξαγωγή των συντελεστών MFCC έτσι ώστε να είναι εφικτός ο υπολογισμός της διαμόρφωσης της ενέργειας σήματος στα 4 Ηz. Τα χαρακτηριστικά που εξήχθηκαν, τελικά, είναι τα παρακάτω 27 και θα αναλυθούν στη συνέχεια. %pinakas me arxikes kai telikes diastaseis xarakthristikwn % \begin{center} @@ -74,7 +74,7 @@ Qassim University, Saudi Arabia },είναι μία μετρική που χρη \subsection{Flatness} -To flatness ή αλλιώς επιπεδότητα του ήχου, είναι μία μετρική η οποία χρησιμοποιείται στην ανάλυση ψηφιακών σημάτων για να χαρακτηρίσει το φάσμα ενός ηχητικού σήματος. Συνήθως μετριέται σε decibels(DB), και αποτελεί έναν τρόπο να ποσοτικοποιήσει το πόσο κοντά είναι ένας ήχος σε θόρυβο και πόσο σε τονικότητα. \footnote{https://en.wikipedia.org/wiki/Spectral\_flatness} Η αναφορά στην τονικότητα γίνεται με την έννοια του αρίθμού των κορυφών σε ένα φάσμα συχνοτήτων που θα υπήρχαν λόγω των πολλαπλών ημιτόνων σε αντίθεση με το επίπεδο φάσμα του λευκού θορύβου. Τα μουσικά σήματα, τείνουν να αποτελούνται από πολλαπλούς τόνους, ο καθένας με την δική του κατανομή αρμονικών ενώ στην φωνή δεν εμφανίζεται αυτό. +To flatness ή αλλιώς επιπεδότητα του ήχου, είναι μία μετρική η οποία χρησιμοποιείται στην ανάλυση ψηφιακών σημάτων για να χαρακτηρίσει το φάσμα ενός ηχητικού σήματος. Συνήθως μετριέται σε decibels (DB), και αποτελεί έναν τρόπο να ποσοτικοποιήσει το πόσο κοντά είναι ένας ήχος σε θόρυβο και πόσο σε τονικότητα. \footnote{https://en.wikipedia.org/wiki/Spectral\_flatness} Η αναφορά στην τονικότητα γίνεται με την έννοια του αρίθμού των κορυφών σε ένα φάσμα συχνοτήτων που θα υπήρχαν λόγω των πολλαπλών ημιτόνων σε αντίθεση με το επίπεδο φάσμα του λευκού θορύβου. Τα μουσικά σήματα, τείνουν να αποτελούνται από πολλαπλούς τόνους, ο καθένας με την δική του κατανομή αρμονικών ενώ στην φωνή δεν εμφανίζεται αυτό. \subsection{Perceptual attack time} @@ -115,44 +115,69 @@ To flatness ή αλλιώς επιπεδότητα του ήχου, είναι \end{figure} +\section{Προεπεξεργασία} +Για την προεπεξεργασία των δεδομένων, δοκιμάστηκαν διάφορες τεχνικές έτσι ώστε να βρεθεί ο βέλτιστος συνδυασμός μεθόδων. Οι μέθοδοι που δοκιμάστηκαν είναι οι κλιμακοποίηση, κανονικοποίηση, μείωση μεταβλητών (PCA?) και ο συνδυασμός τους μαζί με κάποιες παραμέτρους. Τα αποτελέσματα παρουσιάζονται στον πίνακα \ref{table:tab} +Στην κλιμακοποίηση όλα τα χαρακτηριστικά έχουν μέσο 0 και απόκλιση ίση με 1 ενώ στην κανονικοποίηση, μετατρέπονται οι τιμές τους ώστε να ανήκουν στο εύρος [0,1] + +\begin{table}[H] +\begin{tabular}{|l|l|l|} +\hline +\textbf{Μέθοδος} & \textbf{Τρόπος προεπεξεργασίας} & \textbf{Ακρίβεια} \\ \hline + & Χωρίς προεπεξεργασία & 0.49 \\ \cline{2-3} + & Κλιμακοποίηση & \textbf{0.89} \\ \cline{2-3} + & Κανονικοποίηση & 0.49 \\ \cline{2-3} + & Κλιμακοποίηση και μετά κανονικοποίηση & 0.78 \\ \cline{2-3} + & Μείωση μεταβλητών και κλιμακοποίηση & 0.88 \\ \cline{2-3} + & perReduced Dataset(?) και κλιμακοποίηση & 0.81 \\ \cline{2-3} + & Μείωση μεταβλητών, κλιμακοποίηση και gamma=scale & 0.88 \\ \cline{2-3} + & Μείωση μεταβλητών, κλιμακοποίηση και sigmoid kernel & 0.58 \\ \cline{2-3} +\multirow{-9}{*}{SVM} & Μείωση μεταβλητών, κλιμακοποίηση και poly kernel dgr & 0.84 \\ \hline +Decision Tree & Μείωση μεταβλητών και κλιμακοποίηση & 0.75 \\ \hline +Multi-Layer Perceptron & Μείωση μεταβλητών, κλιμακοποίηση και rndState = 2 & 0.86 \\ \hline +Naive Bayes & Μείωση μεταβλητών και κλιμακοποίηση & 0.65 \\ \hline +\end{tabular} +\caption{Τρόποι προεπεξεργασίας για διάφορα μοντέλα} +\label{table:tab} +\end{table} + Eπιπλέον, ελέγξαμε όλα τα χαρακτηριστικά ένα προς ένα έτσι ώστε να δούμε εάν υπάρχει τρόπος να φτάσουμε την πλήρη ακρίβεια του μοντέλου μας με λιγότερα ή να φτάσουμε αρκετά κοντά ίσως ακόμα και με μόνο ένα χαρακτηριστικό. Tα αποτελέσματα έδειξαν ότι κανένα χαρακτηριστικό από μόνο του δεν είναι ικανό να δώσει ικανοποιητικό ποσοστό ακρίβειας στο μοντέλο. Αν πάρουμε το καλύτερο σε όρους ακρίβειας και το δοκιμάσουμε σε συνδυασμό με τα επόμενα καλύτερα, φαίνεται ότι η ακρίβεια αυξάνεται λίγο αλλά όχι αρκετά. Τέλος, αν επαναληφθεί ακόμα μία φορά η διαδικασία, φαίνεται ότι έχουμε και πάλι μια μικρή αύξηση στην ακρίβεια, η οποία όμως είναι αρκετά μακρυά από την ακρίβεια που επιτυγχάνεται εν τέλει. \begin{table}[H] -\begin{tabular}{llll} -{\ul \textbf{Accuracy}} & {\ul \textbf{Individually}} & {\ul \textbf{with best 1}} & {\ul \textbf{with best 2}} \\ -4Hz Mod & 0.58 & 0.66 & 0.73 \\ -Flat & 0.63 & 0.71 & 0.75 \\ -HFC & 0.58 & 0.65 & 0.72 \\ -LAtt & 0.62 & 0.71 & 0.75 \\ -SC & 0.59 & 0.66 & 0.73 \\ -Scomp & 0.57 & 0.66 & 0.73 \\ -SDec & 0.63 & 0.65 & 0.72 \\ -SEFlat & 0.51 & 0.65 & 0.72 \\ -SF & 0.55 & 0.69 & 0.75 \\ -SFlat & 0.57 & 0.66 & 0.72 \\ -SLAtt & 0.63 & 0.71 & 0.74 \\ -SR & 0.60 & 0.66 & 0.72 \\ -SSDec & \textbf{0.65} & - & - \\ -ZCR & 0.58 & 0.65 & 0.72 \\ -mfcc0 & 0.61 & 0.66 & 0.73 \\ -mfcc1 & 0.58 & 0.67 & 0.73 \\ -mfcc2 & 0.52 & 0.66 & 0.73 \\ -mfcc3 & 0.56 & 0.69 & 0.76 \\ -mfcc4 & 0.54 & 0.67 & 0.74 \\ -mfcc5 & 0.57 & 0.70 & 0.75 \\ -mfcc6 & 0.61 & \textbf{0.72} & - \\ -mfcc7 & 0.57 & 0.68 & 0.75 \\ -mfcc8 & 0.55 & 0.67 & 0.74 \\ -mfcc9 & 0.54 & 0.67 & 0.73 \\ -mfcc10 & 0.54 & 0.65 & 0.73 \\ -mfcc11 & 0.51 & 0.66 & 0.73 \\ -mfcc12 & 0.54 & 0.67 & 0.73 +\begin{tabular}{|l|l|l|l|} +\hline +\textbf{Accuracy} & \textbf{Individually} & \textbf{with best 1} & \textbf{with best 2} \\\hline +4Hz Mod & 0.58 & 0.66 & 0.73 \\\hline +Flat & 0.63 & 0.71 & 0.75 \\\hline +HFC & 0.58 & 0.65 & 0.72 \\\hline +LAtt & 0.62 & 0.71 & 0.75 \\\hline +SC & 0.59 & 0.66 & 0.73 \\\hline +Scomp & 0.57 & 0.66 & 0.73 \\\hline +SDec & 0.63 & 0.65 & 0.72 \\\hline +SEFlat & 0.51 & 0.65 & 0.72 \\\hline +SF & 0.55 & 0.69 & 0.75 \\\hline +SFlat & 0.57 & 0.66 & 0.72 \\\hline +SLAtt & 0.63 & 0.71 & 0.74 \\\hline +SR & 0.60 & 0.66 & 0.72 \\\hline +SSDec & \textbf{0.65} & - & - \\\hline +ZCR & 0.58 & 0.65 & 0.72 \\\hline +mfcc0 & 0.61 & 0.66 & 0.73 \\\hline +mfcc1 & 0.58 & 0.67 & 0.73 \\\hline +mfcc2 & 0.52 & 0.66 & 0.73 \\\hline +mfcc3 & 0.56 & 0.69 & 0.76 \\\hline +mfcc4 & 0.54 & 0.67 & 0.74 \\\hline +mfcc5 & 0.57 & 0.70 & 0.75 \\\hline +mfcc6 & 0.61 & \textbf{0.72} & - \\\hline +mfcc7 & 0.57 & 0.68 & 0.75 \\\hline +mfcc8 & 0.55 & 0.67 & 0.74 \\\hline +mfcc9 & 0.54 & 0.67 & 0.73 \\\hline +mfcc10 & 0.54 & 0.65 & 0.73 \\\hline +mfcc11 & 0.51 & 0.66 & 0.73 \\\hline +mfcc12 & 0.54 & 0.67 & 0.73 \\\hline \end{tabular} \caption{Ακρίβεια μεμονωμένων χαρακτηριστικών και συνδυασμών τους} \end{table} - %Διαγραμμα features-απόδοσης; Άρα, είναι προφανές ότι δεν είναι κάποιο συγκεκριμένο χαρακτηριστικό το οποίο ευθύνεται για το μεγαλύτερο ποσοστό της ακρίβειας του μοντέλου αλλά ο συνδυασμός τους. diff --git a/presentation/main.pdf b/presentation/main.pdf index a4bd4c4ac571f39ba09cb6c084bef615acf0d0eb..1746bc0aa45ffeb1b2c890c0b3a8a664dca731d9 100644 GIT binary patch delta 77751 zcmZs>V~j3Lv^ChaZCj`9?$fqy+jjTUwr$(CZQHg_oAZ8i=f};={8&k)lATJWD%pFX zYJ=(^k{X#@K}3w6iGdA<{P_On4Tgm!0ht`Y!OF$Y5993OWNK&&@)e-xT9m)Tb zH$<-=E{p?MSk(VZv=e9xoZ4h!t7QC>1A7+Ebw16BxC_^iSc5hyJBTn0FY(szE`R9_ 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