From cdff3d91e0e34e260209f30cd10ed88117cbe7d8 Mon Sep 17 00:00:00 2001 From: Apostolof Date: Sat, 19 Jan 2019 12:45:03 +0200 Subject: [PATCH] Fix footnotes --- report/1.intro.tex | 2 +- report/3.features_and_preprocessing.tex | 6 +++--- report/4.models.tex | 2 +- report/main.pdf | Bin 672343 -> 672306 bytes 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/report/1.intro.tex b/report/1.intro.tex index ae8b2b4..0898f33 100644 --- a/report/1.intro.tex +++ b/report/1.intro.tex @@ -4,7 +4,7 @@ μάθησης το οποίο, παρέχοντας ένα αρχείο ήχου, θα μπορεί να ξεχωρίσει ανάμεσα στα κομμάτια του χρόνου που περιέχουν ομιλία (speech) και μουσική (music), όπως παρουσιάζεται στον διαγωνισμό MIREX 2018:Music and/or Speech Detection -\footnote{https://www.music-ir.org/mirex/wiki/2018:Music\_and/or\_Speech\_Detection} . +\footnote{\href{https://www.music-ir.org/mirex/wiki/2018:Music_and/or_Speech_Detection}{MIREX - 2018:Music and/or Speech Detection}, last accessed: \today}. Η εργασία επικεντρώνεται στην εύρεση των δειγμάτων που περιέχουν είτε ομιλία είτε μουσική και στην ταξινόμησή τους. diff --git a/report/3.features_and_preprocessing.tex b/report/3.features_and_preprocessing.tex index 9a00bcb..0a1d986 100644 --- a/report/3.features_and_preprocessing.tex +++ b/report/3.features_and_preprocessing.tex @@ -2,7 +2,7 @@ Η υλοποίηση αναπτύχθηκε στη γλώσσα Python 3 και χρησιμοποιήθηκε πληθώρα βιβλιοθηκών (modules) όπως η essentia για την εξαγωγή χαρακτηριστικών, η scikit-learn για την προεπεξεργασία δεδομένων και την εκπαίδευση των μοντέλων, η seaborn για την δημιουργία διαγραμμάτων και την οπτικοποίηση των χαρακτηριστικών. Παράλληλα, σε συνδυασμό με όλες αυτές χρησιμοποιήθηκαν και άλλες βιβλιοθήκες όπως η numpy, η pandas, η matplotlib, η multiprocessing, η οs, η pyaudio και άλλες. Για την εκπαίδευση, δοκιμάστηκαν τα μοντέλα SVM, Decision Trees, Multilayer Perceptron, Naive Bayes και Random Forest, λεπτομέριες για τα οποία θα αναφερθούν στα επόμενα κεφάλαια. -Το dataset που χρησιμοποιήθηκε για την εκπαίδευση του μοντέλου είναι το προτεινόμενο GTZAN dataset \footnote{http://opihi.cs.uvic.ca/sound/music\_speech.tar.gz}, το οποίο αποτελείται από 128 αρχεία διάρκειας 30 δευτερολέπτων. Κάθε κλάση (μουσική/φωνή) αποτελείται από 64 αρχεία ενώ δεν υπάρχουν αρχεία που να περιέχουν και τις δύο κλάσεις. Όλα τα αρχεία ήχου είναι δειγματοληπτημένα στα 22050 Hz, μονοκάναλα, με βάθος ήχου 16-bit και σε μορφή WAV. +Το dataset που χρησιμοποιήθηκε για την εκπαίδευση του μοντέλου είναι το προτεινόμενο GTZAN dataset \footnote{\href{http://opihi.cs.uvic.ca/sound/music_speech.tar.gz}{GTZAN dataset download}, last accessed: \today}, το οποίο αποτελείται από 128 αρχεία διάρκειας 30 δευτερολέπτων. Κάθε κλάση (μουσική/φωνή) αποτελείται από 64 αρχεία ενώ δεν υπάρχουν αρχεία που να περιέχουν και τις δύο κλάσεις. Όλα τα αρχεία ήχου είναι δειγματοληπτημένα στα 22050 Hz, μονοκάναλα, με βάθος ήχου 16-bit και σε μορφή WAV. \section{Χαρακτηριστικά} @@ -40,7 +40,7 @@ Speech and Music Classification and Separation: A Review, Abdullah I. Al-Shoshan \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 ποσοτικοποίησης του 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 αριθμού των κορυφών σε ένα φάσμα συχνοτήτων που θα υπήρχαν λόγω των πολλαπλών ημίτονων 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 ομιλία 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 ποσοτικοποίησης του πόσο κοντά είναι ένας ήχος σε θόρυβο και πόσο σε τονικότητα. Η αναφορά στην τονικότητα γίνεται με την έννοια του αριθμού των κορυφών σε ένα φάσμα συχνοτήτων που θα υπήρχαν λόγω των πολλαπλών ημίτονων 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 ομιλία δεν εμφανίζεται αυτό. \subsection{Perceptual attack time} @@ -60,7 +60,7 @@ To spectral complexity ή αλλιώς η φασματική πολυπλοκό \subsection{4Hz Energy Modulation} -Τα σήματα ομιλίας έχουν χαρακτηριστικό μέγιστο στη διαμόρφωση ενέργειας γύρω στα 4Hz του ρυθμού συλλαβών. Για να μοντελοποιηθεί αυτή η ιδιότητα ακολουθείται η παρακάτω διαδικασία\footnote{https://www.irit.fr/recherches/SAMOVA/FeaturesExtraction.html\#me4hz}: το σήμα τμηματοποιείται σε frames, εξάγονται οι συντελεστές Mel Frequency Spectrum και υπολογίζεται η ενέργεια σε 40 κανάλια αντίληψης. Αυτή η ενέργεια έπειτα φιλτράρεται με ένα ζωνοδιαβατό φίλτρο, κεντραρισμένο στα 4Hz. Η ενέργεια αθροίζεται για όλα τα κανάλια, και κανονικοποιείται με βάση τη μέση ενέργεια του κάθε frame. Η διαμόρφωση δίνεται από το κανονικοποιημένο άθροισμα της φιλτραρισμένης ενέργειας. Η φωνή περιέχει περισσότερη διαμόρφωση από την μουσική. +Τα σήματα ομιλίας έχουν χαρακτηριστικό μέγιστο στη διαμόρφωση ενέργειας γύρω στα 4Hz του ρυθμού συλλαβών. Για να μοντελοποιηθεί αυτή η ιδιότητα ακολουθείται η παρακάτω διαδικασία\footnote{\href{https://www.irit.fr/recherches/SAMOVA/FeaturesExtraction.html\#me4hz}{IRIT/SAMoVA - 4 Hz modulation energy}, last accessed: \today}: το σήμα τμηματοποιείται σε frames, εξάγονται οι συντελεστές Mel Frequency Spectrum και υπολογίζεται η ενέργεια σε 40 κανάλια αντίληψης. Αυτή η ενέργεια έπειτα φιλτράρεται με ένα ζωνοδιαβατό φίλτρο, κεντραρισμένο στα 4Hz. Η ενέργεια αθροίζεται για όλα τα κανάλια, και κανονικοποιείται με βάση τη μέση ενέργεια του κάθε frame. Η διαμόρφωση δίνεται από το κανονικοποιημένο άθροισμα της φιλτραρισμένης ενέργειας. Η φωνή περιέχει περισσότερη διαμόρφωση από την μουσική. \vspace{1em} Στα διαγράμματα [\ref{featureTable:table1}] και [\ref{featureTable:table2}] φαίνονται ενδεικτικά κάποια από τα χαρακτηριστικά που υπολογίστηκαν και το πόσο αποτελεσματικά είναι στον διαχωρισμό των κλάσεων. diff --git a/report/4.models.tex b/report/4.models.tex index 3aafa06..47cf202 100644 --- a/report/4.models.tex +++ b/report/4.models.tex @@ -1,6 +1,6 @@ \section{Machine Learning Models} -Στη συνέχεια αναφέρεται συνοπτικά η λειτουργία των μοντέλων που δοκιμάστηκαν για την εκπαίδευση των δεδομένων (οι ορισμοί είναι σύμφωνα με την ιστοσελίδα της analytics vidhya \footnote{https://www.analyticsvidhya.com/}) ενώ στο τέλος παρατίθεται ένας πίνακας στον οποίο φαίνονται οι διάφορες μέθοδοι και οι ακρίβειες που επιτεύχθηκαν. +Στη συνέχεια αναφέρεται συνοπτικά η λειτουργία των μοντέλων που δοκιμάστηκαν για την εκπαίδευση των δεδομένων (οι ορισμοί είναι σύμφωνα με την ιστοσελίδα της Analytics Vidhya \footnote{\href{https://www.analyticsvidhya.com/}{Analytics Vidhya}, last accessed: \today}) ενώ στο τέλος παρατίθεται ένας πίνακας στον οποίο φαίνονται οι διάφορες μέθοδοι και οι ακρίβειες που επιτεύχθηκαν. \subsection{Support Vector Machine - SVM} diff --git a/report/main.pdf b/report/main.pdf index e5b01412801c945314fbe753beb1fe0dc073c373..976108d0b5f8b54e2f3879c5e057dfbbaeb5ba69 100644 GIT binary patch delta 57742 zcmZVEV|So!*d^%Lwry5y+qP|+cUZCQRK>P!+eyW?D;4)M(>-gwz2?LI4c9)8Yp;dT zHipsGb3uc#|42fm0NB}CxCI5_+}vF)OdR06H&+Nc;>kNWUbN1jJEz}4ARwXa!DmMpWh4x_-n4|EQreycMiiVx|sIJuVQGaEm5&qAl0~1V8t; 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