From afefb3ac1cc5ba360066343583b9c6bec0cf1d6f Mon Sep 17 00:00:00 2001 From: Christina Theodoridou Date: Mon, 14 Jan 2019 13:31:12 +0200 Subject: [PATCH] added our implementetion section in features and preprocessing --- presentation/features_and_preprocessing.tex | 15 +++++++++------ presentation/main.pdf | Bin 667096 -> 662270 bytes presentation/models.tex | 2 +- 3 files changed, 10 insertions(+), 7 deletions(-) diff --git a/presentation/features_and_preprocessing.tex b/presentation/features_and_preprocessing.tex index c28d9f5..c9da309 100644 --- a/presentation/features_and_preprocessing.tex +++ b/presentation/features_and_preprocessing.tex @@ -1,8 +1,13 @@ +\section{Η υλοποίησή μας} + +Η υλοποίησή μας 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 matplotlib για την εξαγωγή διαγραμμάτων και την οπτικοποίηση των χαρακτηριστικών. Τέλος, σε συνδυασμό με όλες αυτές χρησιμοποιήθηκαν και άλλες βιβλιοθήκες όπως η padas, η seaborn, η multiprocessing ,η οs, η pyaudio κα. + + \section{Χαρακτηριστικά και προεπεξεργασία δεδομένων} 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, με την βοήθεια της βιβλιοθήκης essentia.Παράλληλα, έγινε και η εξαγωγή των συντελεστών MFCC έτσι ώστε να είναι εφικτός ο υπολογισμός της διαμόρφωσης της ενέργειας σήματος στα 4 hz. Τα χαρακτηριστικά που εξήχθηκαν είναι 27 και θα αναλυθούν στη συνέχεια. Ως προεπεξεργασία, τα δεδομένα κλιμακοποιήθηκαν με την βοήθεια της συνάρτησης scale που ανήκει στο module preprocessing της βιβλιοθήκης sklearn, έτσι ώστε να έχουν μέσο 0 και τυπική απόκλιση ίση με 1 για κάθε συνιστώσα. Τέλος, με την συνάρτηση PCA του module decomposition του sklearn, δίνουμε την δυνατότητα στον κώδικά μας να εφαρμοστεί και η μέθοδος Principal Component Analysis - PCA,αν αυτή είναι αναγκαία, έτσι ώστε να μειωθούν οι τελικές διαστάσεις των χαρακτηριστικών. Στην δική μας υλοποίηση, μειώνουμε τις διαστάσεις από 27 σε 10, έτσι ώστε να έχουν αξιοσημείωτη μείωση και να ελέγξουμε 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 hz. Τα χαρακτηριστικά που εξήχθηκαν είναι 27 και θα αναλυθούν στη συνέχεια. Ως προεπεξεργασία, τα δεδομένα κλιμακοποιήθηκαν με την βοήθεια της συνάρτησης scale που ανήκει στο module preprocessing της βιβλιοθήκης sklearn, έτσι ώστε να έχουν μέσο 0 και τυπική απόκλιση ίση με 1 για κάθε συνιστώσα. Τέλος, δίνουμε την δυνατότητα στον κώδικά μας να εφαρμοστεί και η μέθοδος Principal Component Analysis - PCA,αν αυτή είναι αναγκαία, έτσι ώστε να μειωθούν οι τελικές διαστάσεις των χαρακτηριστικών. Στην δική μας υλοποίηση, μειώνουμε τις διαστάσεις από 27 σε 10, έτσι ώστε να έχουν αξιοσημείωτη μείωση και να ελέγξουμε τι γίνεται σε αυτήν την περίπτωση με την απόδοση. %pinakas me arxikes kai telikes diastaseis xarakthristikwn % \begin{center} @@ -44,13 +49,11 @@ \subsection{Spectral Centroid - SC} -Το spectral cendroid ή αλλιώς φασματικό κέντρο , όπως αναφέρεται στο Footnote είναι μία μετρική που χρησιμοποιείται ώστε να χαρακτηρίσει ένα φάσμα. Υποδεικνύει πού βρίσκεται το κέντρο του φάσματος. Έχει ισχυρή σύνδεση με την "φωτεινότητα " ενός ήχου δηλαδή με την χροιά. Συνήθως, το κέντρο του φάσματος της φωνής συγκεντρώνεται σε χαμηλές συχνότητες και έπειτα συμπτύσσεται πολύ γήργορα στις υψιλότερες συχνότητες ενώ δεν υπάρχει DC συνιστώσα. Αντίθετα, στην μουσική δεν έχει παρατηρηθεί κάποιο συγκεκριμένο σχήμα του φάσματος. \footnote{ +Το spectral cendroid ή αλλιώς φασματικό κέντρο , όπως αναφέρεται στο \footnote{ Speech and Music Classification and Separation: A Review Abdullah I. Al-Shoshan Department of Computer Science, College of Computer, -Qassim University, Saudi Arabia } - - +Qassim University, Saudi Arabia },είναι μία μετρική που χρησιμοποιείται ώστε να χαρακτηρίσει ένα φάσμα. Υποδεικνύει πού βρίσκεται το κέντρο του φάσματος. Έχει ισχυρή σύνδεση με την "φωτεινότητα " ενός ήχου δηλαδή με την χροιά. Συνήθως, το κέντρο του φάσματος της φωνής συγκεντρώνεται σε χαμηλές συχνότητες και έπειτα συμπτύσσεται πολύ γήργορα στις υψιλότερες συχνότητες ενώ δεν υπάρχει DC συνιστώσα. Αντίθετα, στην μουσική δεν έχει παρατηρηθεί κάποιο συγκεκριμένο σχήμα του φάσματος. \subsection{Roll Off} Το συγκικριμένο χαρακτηριστικό αναπαριστά την τιμή της συχνότητας, κάτω από την οποία βρίσκεται το 95\% της ενέργειας του σήματος. Όπως προαναφέρθηκε, η ενέργεια του μουσικού σήματος συγκεντρώνεται σε υψηλότερες συχνότητες σε σχέση με το φωνητικό σήμα. 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100644 --- a/presentation/models.tex +++ b/presentation/models.tex @@ -1,6 +1,6 @@ \section{Machine Learning Model} -Για την υλοποίηση του ταξινομητή δοκιμάστηκαν διάφορες μέθοδοι οι οποίες πετυχαίνουν διαφορετική ακρίβεια. Για την εκπαίδευση των μοντέλων, χρησιμοποιήθηκε η βιβλιοθήκη numpy και sklearn. Επίσης , για την μέτρηση της απόδοσης των μοντέλων εφαρμόστηκε η μέθοδος K-Fold cross validation.Στη συνέχεια αναφέρεται συνοπτικά η λειτουργία των μοντέλων που χρησιμοποιήθηκαν (Οι ορισμοί είναι σύμφωνα με την ιστοσελίδα της analytics vidhya \footnote{https://www.analyticsvidhya.com/}) ενώ στο τέλος παρατίθεται ένας πίνακας στο οποίο φαίνονται οι διάφορες μέθοδοι και οι ακρίβειες που πέτυχαν. +Για την υλοποίηση του ταξινομητή δοκιμάστηκαν διάφορες μέθοδοι οι οποίες πετυχαίνουν διαφορετική ακρίβεια.Επίσης , για την μέτρηση της απόδοσης των μοντέλων εφαρμόστηκε η μέθοδος K-Fold cross validation.Στη συνέχεια αναφέρεται συνοπτικά η λειτουργία των μοντέλων που χρησιμοποιήθηκαν (Οι ορισμοί είναι σύμφωνα με την ιστοσελίδα της analytics vidhya \footnote{https://www.analyticsvidhya.com/}) ενώ στο τέλος παρατίθεται ένας πίνακας στο οποίο φαίνονται οι διάφορες μέθοδοι και οι ακρίβειες που πέτυχαν. \subsection{Support Vector Machine - SVM}