Speech Recognition Combining MFCCs and Image Features
|
|
- Lenard Henry
- 5 years ago
- Views:
Transcription
1 Speech Recognition Combining MFCCs and Image Featres S. Karlos from Department of Mathematics N. Fazakis from Department of Electrical and Compter Engineering K. Karanikola from Department of Mathematics S. Kotsiantis from Department of Mathematics K. Sgarbas from Department of Electrical and Compter Enginnering University of Patras, Greece
2 Aim Combination of adio signal and image featres Exploitation of larger frames for speech signals Increase of classification accracy withot sing complex algorithms
3 Contents Speaker Identification problem Attribtes of speech signals Examine Content Based Image Featres (CBIR) Combination of MFCCs + CBIR Experiments Conclsion
4 Speaker Identification Problem Determines the speaker from a set of registered speakers q This is called a closed set identification q Reslt is the best speaker matched What if the speaker is not in the database? q This is called an open set identification q Reslt can be a speaker or a no-match reslt Or experiment is a closed set identification problem
5 Extraction of adio characteristics Different representations of speech signals: 1. Mel-Freqency Cepstral Coefficients (MFCC) 2. Linear Predictive Codes (LPCs) 3. Perceptal Linear Prediction (PLP) 4. PLP-Relative Spectra (PLP-RASTA) Non-linear behavior of speech Need for adapting signal to hman ear scale Most efficient soltion: MFCCs featres
6 Extraction of image characteristics Spectrogram: time-freqency representation of an adio signal Short-Term Forier Transform (STFT) Different approaches of image processing : 1. Content-Based 2. Featre-Based 3. Appearance-Based Determine the similarity throgh distances of featre vectors
7 Related works Content Based Image Processing (CBIR) techniqes have been widely sed Exploitation of color content and textre information Most known approaches: 1. Local gradient featres along with PCA + HMMs 2. Delta MFCCs 3. 2D Gabor Featres + MLP 4. Featre-Finding Neral Network (FFNN) 5. Wavelet package transform + MKL 6. RANSAC algorithm
8 Proposed Techniqe 1 st view Acqire the first 25 coefficients of MFCCs (0 th has been rejected) Hamming window has been preferred Time dration of each frame eqals to 0.5 seconds Overlap factor eqals to 50% Highest band edge of Mel filters eqals to 4kHz Use of 40 warped spectral bands Logarithmical scale of magnitde spectrm Discrete Cosine Transformation (DCT)
9 Proposed Techniqe 2 nd view Use of AtoColorCorrelogramFilter (atocor) a # " I = γ # # "," I, γ "),"* I = Pr.) 0"),.* 0 p * I "2 dist p ), p * = k Spatial correlation of colors from each image is distilled Not based on prely local properties Effective in recognizing large changes of shape Efficiently compted
10 MFCCs + atocor + SVM
11 Proposed Techniqe Learning stage Spport Vector Machines (SVMs) Hyperplanes that separate two classes Maximizing the margin for redcing the generalization error Can deal with very high dimensional data Efficient implementation throgh LibSVM library Use of polynomial kernel (degree = 3)
12 Data CHAINS Corps Selected mode: Solo speech 36 speakers (28 from Eastern Ireland 8 from UK and USA) 19 different sentences ot of the 33 3 scenarios: 8, 16 and 36 speakers Eqal male and female speakers dring each scenario
13 Experimental procedre Comparison with another 9 image filters Spervised classifiers: 1. SVMs 2. Mlti-Layer Perceptron (MLP) 3. Logistic Regression (LogReg) 10-cross-validation techniqe WEKA tool was sed along with libraries of Lcene Image Retrieval (LIRe) Record comptational time (Intel i3 64bit system - 8GB RAM)
14 Experimental procedre CBIR Filters Initial Nmber of featres Usefl Nmber of featres atocor binpyr clay edhist fcth fzzy gabor jpeg phog simplehist Redction of dimensionality: Remove seless attribtes Size of datasets on instances has been redced dramatically: q 8speakers: abot > q 16speakers: abot > q 36speakers: abot > 5.818
15 Reslts 8 speakers 16 speakers 36 speakers Classifiers MFCCs MFCCs + atocor MFCCs MFCCs + atocor MFCCs MFCCs + atocor SVM Time(sec) MLP Time(sec) LogReg Time(sec)
16 Statistical comparison q q Post-hoc test of Nemenyi CD s length depicts the needed distance for significant difference
17 Experiments A boost of accracy was recorded for all the tested scenarios 11.5%, 7.8% and 9.9% improvement compared with standalone MFCCs Bilding of classification model demands a few seconds Fzzy filtering techniqes performed flctations MFCCs+atocor and MFCCs+binpyr achieved the best reslts The proposed techniqe reqires mch less comptational resorces
18 Conclsions Tackle with Atomatic Speech Recognition (ASR) tasks Increase the featre vector of adio signals Redce the training time Methods based on local featres performed poor reslts Improved generalization behavior for the most SI filters
19 Promising points Extract more specialized featres nder MFCCs + SI featres scheme Parallel implementation Apply mlti-view Semi-spervised techniqes Combination of magnitde with phase related featres (Hartley Phase Spectrm)
20 References M. Lx and S. A. Chatzichristofis, Lire: lcene image retrieval, Proceeding 16th ACM Int. Conf. Mltimed. - MM 08, p. 1085, F. Cmmins, M. Grimaldi, T. Leonard, and J. Simko, The CHAINS Speech Corps: CHAracterizing INdividal Speakers, Proc SPECOM, pp. 1 6, 2006 J. Dennis, H. D. Tran, and H. Li, Spectrogram Image Featre for Sond Event Classification in Mismatched Conditions, IEEE Signal Process. Lett., vol. 18, no. 2, pp , Feb M. Mayo, ImageFilter WEKA filter that ses LIRE to extract image featres, [Online]. Available: I. Paraskevas and M. Rangossi, The hartley phase spectrm as an assistive featre for classification, Lect. Notes Compt. Sci. (inclding Sbser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol LNAI, pp , 2010
INTER GENRE SIMILARITY MODELLING FOR AUTOMATIC MUSIC GENRE CLASSIFICATION
INTER GENRE SIMILARITY MODELLING FOR AUTOMATIC MUSIC GENRE CLASSIFICATION ULAŞ BAĞCI AND ENGIN ERZIN arxiv:0907.3220v1 [cs.sd] 18 Jul 2009 ABSTRACT. Music genre classification is an essential tool for
More informationInternational Journal of Advance Engineering and Research Development MUSICAL INSTRUMENT IDENTIFICATION AND STATUS FINDING WITH MFCC
Scientific Journal of Impact Factor (SJIF): 5.71 International Journal of Advance Engineering and Research Development Volume 5, Issue 04, April -2018 e-issn (O): 2348-4470 p-issn (P): 2348-6406 MUSICAL
More informationInstrument Recognition in Polyphonic Mixtures Using Spectral Envelopes
Instrument Recognition in Polyphonic Mixtures Using Spectral Envelopes hello Jay Biernat Third author University of Rochester University of Rochester Affiliation3 words jbiernat@ur.rochester.edu author3@ismir.edu
More informationAutomatic Rhythmic Notation from Single Voice Audio Sources
Automatic Rhythmic Notation from Single Voice Audio Sources Jack O Reilly, Shashwat Udit Introduction In this project we used machine learning technique to make estimations of rhythmic notation of a sung
More informationSpeech and Speaker Recognition for the Command of an Industrial Robot
Speech and Speaker Recognition for the Command of an Industrial Robot CLAUDIA MOISA*, HELGA SILAGHI*, ANDREI SILAGHI** *Dept. of Electric Drives and Automation University of Oradea University Street, nr.
More informationChord Classification of an Audio Signal using Artificial Neural Network
Chord Classification of an Audio Signal using Artificial Neural Network Ronesh Shrestha Student, Department of Electrical and Electronic Engineering, Kathmandu University, Dhulikhel, Nepal ---------------------------------------------------------------------***---------------------------------------------------------------------
More informationSinger Identification
Singer Identification Bertrand SCHERRER McGill University March 15, 2007 Bertrand SCHERRER (McGill University) Singer Identification March 15, 2007 1 / 27 Outline 1 Introduction Applications Challenges
More informationAutomatic Identification of Instrument Type in Music Signal using Wavelet and MFCC
Automatic Identification of Instrument Type in Music Signal using Wavelet and MFCC Arijit Ghosal, Rudrasis Chakraborty, Bibhas Chandra Dhara +, and Sanjoy Kumar Saha! * CSE Dept., Institute of Technology
More informationMUSICAL INSTRUMENT RECOGNITION WITH WAVELET ENVELOPES
MUSICAL INSTRUMENT RECOGNITION WITH WAVELET ENVELOPES PACS: 43.60.Lq Hacihabiboglu, Huseyin 1,2 ; Canagarajah C. Nishan 2 1 Sonic Arts Research Centre (SARC) School of Computer Science Queen s University
More informationBrain-actuated Control of Wheelchair Using Fuzzy Neural Networks
Int'l Conf. Artificial Intelligence ICAI'6 67 Brain-actated Control of Wheelchair Using Fzzy Neral Networks Rahib H.Abiyev, Nrllah Akkaya, Ersin Aytac, Irfan Günsel, Ahmet Ça man, Sanan Abizade Near East
More informationAUTOREGRESSIVE MFCC MODELS FOR GENRE CLASSIFICATION IMPROVED BY HARMONIC-PERCUSSION SEPARATION
AUTOREGRESSIVE MFCC MODELS FOR GENRE CLASSIFICATION IMPROVED BY HARMONIC-PERCUSSION SEPARATION Halfdan Rump, Shigeki Miyabe, Emiru Tsunoo, Nobukata Ono, Shigeki Sagama The University of Tokyo, Graduate
More informationA Parallel Multilevel-Huffman Decompression Scheme for IP Cores with Multiple Scan Chains
A Parallel Mltilevel-Hffman Decompression Scheme for IP Cores with Mltiple Scan Chains X Kavosianos, E Kalligeros 2 and D Nikolos 2 Compter Science Dept, University of Ioannina, 45 Ioannina, Greece 2 Compter
More informationSemi-supervised Musical Instrument Recognition
Semi-supervised Musical Instrument Recognition Master s Thesis Presentation Aleksandr Diment 1 1 Tampere niversity of Technology, Finland Supervisors: Adj.Prof. Tuomas Virtanen, MSc Toni Heittola 17 May
More informationPRODUCTION MACHINERY UTILIZATION MONITORING BASED ON ACOUSTIC AND VIBRATION SIGNAL ANALYSIS
8th International DAAAM Baltic Conference "INDUSTRIAL ENGINEERING" 19-21 April 2012, Tallinn, Estonia PRODUCTION MACHINERY UTILIZATION MONITORING BASED ON ACOUSTIC AND VIBRATION SIGNAL ANALYSIS Astapov,
More informationAutomatic Laughter Detection
Automatic Laughter Detection Mary Knox Final Project (EECS 94) knoxm@eecs.berkeley.edu December 1, 006 1 Introduction Laughter is a powerful cue in communication. It communicates to listeners the emotional
More informationRecognising Cello Performers using Timbre Models
Recognising Cello Performers using Timbre Models Chudy, Magdalena; Dixon, Simon For additional information about this publication click this link. http://qmro.qmul.ac.uk/jspui/handle/123456789/5013 Information
More informationImproving Frame Based Automatic Laughter Detection
Improving Frame Based Automatic Laughter Detection Mary Knox EE225D Class Project knoxm@eecs.berkeley.edu December 13, 2007 Abstract Laughter recognition is an underexplored area of research. My goal for
More informationClassification of Musical Instruments sounds by Using MFCC and Timbral Audio Descriptors
Classification of Musical Instruments sounds by Using MFCC and Timbral Audio Descriptors Priyanka S. Jadhav M.E. (Computer Engineering) G. H. Raisoni College of Engg. & Mgmt. Wagholi, Pune, India E-mail:
More informationMusical Instrument Identification Using Principal Component Analysis and Multi-Layered Perceptrons
Musical Instrument Identification Using Principal Component Analysis and Multi-Layered Perceptrons Róisín Loughran roisin.loughran@ul.ie Jacqueline Walker jacqueline.walker@ul.ie Michael O Neill University
More informationA Real-time Framework for Video Time and Pitch Scale Modification
Dblin Institte of Technology ARROW@DIT Conference papers Adio Research Grop 2008-06-01 A Real-time Framework for Video Time and Pitch Scale Modification Ivan Damnjanovic Qeen Mary University London Dan
More informationMUSI-6201 Computational Music Analysis
MUSI-6201 Computational Music Analysis Part 9.1: Genre Classification alexander lerch November 4, 2015 temporal analysis overview text book Chapter 8: Musical Genre, Similarity, and Mood (pp. 151 155)
More informationABSOLUTE OR RELATIVE? A NEW APPROACH TO BUILDING FEATURE VECTORS FOR EMOTION TRACKING IN MUSIC
ABSOLUTE OR RELATIVE? A NEW APPROACH TO BUILDING FEATURE VECTORS FOR EMOTION TRACKING IN MUSIC Vaiva Imbrasaitė, Peter Robinson Computer Laboratory, University of Cambridge, UK Vaiva.Imbrasaite@cl.cam.ac.uk
More informationA CLASSIFICATION APPROACH TO MELODY TRANSCRIPTION
A CLASSIFICATION APPROACH TO MELODY TRANSCRIPTION Graham E. Poliner and Daniel P.W. Ellis LabROSA, Dept. of Electrical Engineering Columbia University, New York NY 127 USA {graham,dpwe}@ee.columbia.edu
More informationSinger Traits Identification using Deep Neural Network
Singer Traits Identification using Deep Neural Network Zhengshan Shi Center for Computer Research in Music and Acoustics Stanford University kittyshi@stanford.edu Abstract The author investigates automatic
More informationMultimodal Music Mood Classification Framework for Christian Kokborok Music
Journal of Engineering Technology (ISSN. 0747-9964) Volume 8, Issue 1, Jan. 2019, PP.506-515 Multimodal Music Mood Classification Framework for Christian Kokborok Music Sanchali Das 1*, Sambit Satpathy
More informationAutomatic Laughter Detection
Automatic Laughter Detection Mary Knox 1803707 knoxm@eecs.berkeley.edu December 1, 006 Abstract We built a system to automatically detect laughter from acoustic features of audio. To implement the system,
More informationResearch & Development. White Paper WHP 232. A Large Scale Experiment for Mood-based Classification of TV Programmes BRITISH BROADCASTING CORPORATION
Research & Development White Paper WHP 232 September 2012 A Large Scale Experiment for Mood-based Classification of TV Programmes Jana Eggink, Denise Bland BRITISH BROADCASTING CORPORATION White Paper
More informationMUSICAL INSTRUMENT IDENTIFICATION BASED ON HARMONIC TEMPORAL TIMBRE FEATURES
MUSICAL INSTRUMENT IDENTIFICATION BASED ON HARMONIC TEMPORAL TIMBRE FEATURES Jun Wu, Yu Kitano, Stanislaw Andrzej Raczynski, Shigeki Miyabe, Takuya Nishimoto, Nobutaka Ono and Shigeki Sagayama The Graduate
More informationRecognising Cello Performers Using Timbre Models
Recognising Cello Performers Using Timbre Models Magdalena Chudy and Simon Dixon Abstract In this paper, we compare timbre features of various cello performers playing the same instrument in solo cello
More informationMUSICAL NOTE AND INSTRUMENT CLASSIFICATION WITH LIKELIHOOD-FREQUENCY-TIME ANALYSIS AND SUPPORT VECTOR MACHINES
MUSICAL NOTE AND INSTRUMENT CLASSIFICATION WITH LIKELIHOOD-FREQUENCY-TIME ANALYSIS AND SUPPORT VECTOR MACHINES Mehmet Erdal Özbek 1, Claude Delpha 2, and Pierre Duhamel 2 1 Dept. of Electrical and Electronics
More informationAcoustic Scene Classification
Acoustic Scene Classification Marc-Christoph Gerasch Seminar Topics in Computer Music - Acoustic Scene Classification 6/24/2015 1 Outline Acoustic Scene Classification - definition History and state of
More informationComposer Identification of Digital Audio Modeling Content Specific Features Through Markov Models
Composer Identification of Digital Audio Modeling Content Specific Features Through Markov Models Aric Bartle (abartle@stanford.edu) December 14, 2012 1 Background The field of composer recognition has
More informationLB3-PCx50 Premium Cabinet Loudspeakers
Conications Systems LB3-PCx Premim Cabinet Lodspeakers LB3-PCx Premim Cabinet Lodspeakers www.boschsecrity.com High-qality speech and msic reprodction Weatherized, sited for sheltered otside se Prepared
More informationDetecting Musical Key with Supervised Learning
Detecting Musical Key with Supervised Learning Robert Mahieu Department of Electrical Engineering Stanford University rmahieu@stanford.edu Abstract This paper proposes and tests performance of two different
More informationA NOVEL CEPSTRAL REPRESENTATION FOR TIMBRE MODELING OF SOUND SOURCES IN POLYPHONIC MIXTURES
A NOVEL CEPSTRAL REPRESENTATION FOR TIMBRE MODELING OF SOUND SOURCES IN POLYPHONIC MIXTURES Zhiyao Duan 1, Bryan Pardo 2, Laurent Daudet 3 1 Department of Electrical and Computer Engineering, University
More informationVadim V. Romanuke * (Professor, Polish Naval Academy, Gdynia, Poland)
Electrical, Control and Commnication Engineering ISSN 2255-959 (online) ISSN 2255-940 (print) 20, vol. 4, no., pp. 5 57 doi: 0.247/ecce-20-0006 https://www.degryter.com/view/j/ecce An Attempt of Finding
More informationAudio spectrogram representations for processing with Convolutional Neural Networks
Audio spectrogram representations for processing with Convolutional Neural Networks Lonce Wyse 1 1 National University of Singapore arxiv:1706.09559v1 [cs.sd] 29 Jun 2017 One of the decisions that arise
More informationMusic Genre Classification and Variance Comparison on Number of Genres
Music Genre Classification and Variance Comparison on Number of Genres Miguel Francisco, miguelf@stanford.edu Dong Myung Kim, dmk8265@stanford.edu 1 Abstract In this project we apply machine learning techniques
More informationLEARNING AUDIO SHEET MUSIC CORRESPONDENCES. Matthias Dorfer Department of Computational Perception
LEARNING AUDIO SHEET MUSIC CORRESPONDENCES Matthias Dorfer Department of Computational Perception Short Introduction... I am a PhD Candidate in the Department of Computational Perception at Johannes Kepler
More information19 th INTERNATIONAL CONGRESS ON ACOUSTICS MADRID, 2-7 SEPTEMBER 2007
19 th INTERNATIONAL CONGRESS ON ACOUSTICS MADRID, 2-7 SEPTEMBER 2007 AN HMM BASED INVESTIGATION OF DIFFERENCES BETWEEN MUSICAL INSTRUMENTS OF THE SAME TYPE PACS: 43.75.-z Eichner, Matthias; Wolff, Matthias;
More informationA Large Scale Experiment for Mood-Based Classification of TV Programmes
2012 IEEE International Conference on Multimedia and Expo A Large Scale Experiment for Mood-Based Classification of TV Programmes Jana Eggink BBC R&D 56 Wood Lane London, W12 7SB, UK jana.eggink@bbc.co.uk
More informationGCT535- Sound Technology for Multimedia Timbre Analysis. Graduate School of Culture Technology KAIST Juhan Nam
GCT535- Sound Technology for Multimedia Timbre Analysis Graduate School of Culture Technology KAIST Juhan Nam 1 Outlines Timbre Analysis Definition of Timbre Timbre Features Zero-crossing rate Spectral
More informationMood Tracking of Radio Station Broadcasts
Mood Tracking of Radio Station Broadcasts Jacek Grekow Faculty of Computer Science, Bialystok University of Technology, Wiejska 45A, Bialystok 15-351, Poland j.grekow@pb.edu.pl Abstract. This paper presents
More informationBook: Fundamentals of Music Processing. Audio Features. Book: Fundamentals of Music Processing. Book: Fundamentals of Music Processing
Book: Fundamentals of Music Processing Lecture Music Processing Audio Features Meinard Müller International Audio Laboratories Erlangen meinard.mueller@audiolabs-erlangen.de Meinard Müller Fundamentals
More informationCS229 Project Report Polyphonic Piano Transcription
CS229 Project Report Polyphonic Piano Transcription Mohammad Sadegh Ebrahimi Stanford University Jean-Baptiste Boin Stanford University sadegh@stanford.edu jbboin@stanford.edu 1. Introduction In this project
More informationAutomatic Music Genre Classification
Automatic Music Genre Classification Nathan YongHoon Kwon, SUNY Binghamton Ingrid Tchakoua, Jackson State University Matthew Pietrosanu, University of Alberta Freya Fu, Colorado State University Yue Wang,
More informationResearch Article. ISSN (Print) *Corresponding author Shireen Fathima
Scholars Journal of Engineering and Technology (SJET) Sch. J. Eng. Tech., 2014; 2(4C):613-620 Scholars Academic and Scientific Publisher (An International Publisher for Academic and Scientific Resources)
More informationMusic Mood Classification - an SVM based approach. Sebastian Napiorkowski
Music Mood Classification - an SVM based approach Sebastian Napiorkowski Topics on Computer Music (Seminar Report) HPAC - RWTH - SS2015 Contents 1. Motivation 2. Quantification and Definition of Mood 3.
More informationFigure 1: Feature Vector Sequence Generator block diagram.
1 Introduction Figure 1: Feature Vector Sequence Generator block diagram. We propose designing a simple isolated word speech recognition system in Verilog. Our design is naturally divided into two modules.
More informationGRADIENT-BASED MUSICAL FEATURE EXTRACTION BASED ON SCALE-INVARIANT FEATURE TRANSFORM
19th European Signal Processing Conference (EUSIPCO 2011) Barcelona, Spain, August 29 - September 2, 2011 GRADIENT-BASED MUSICAL FEATURE EXTRACTION BASED ON SCALE-INVARIANT FEATURE TRANSFORM Tomoko Matsui
More informationMUSICAL INSTRUMENT RECOGNITION USING BIOLOGICALLY INSPIRED FILTERING OF TEMPORAL DICTIONARY ATOMS
MUSICAL INSTRUMENT RECOGNITION USING BIOLOGICALLY INSPIRED FILTERING OF TEMPORAL DICTIONARY ATOMS Steven K. Tjoa and K. J. Ray Liu Signals and Information Group, Department of Electrical and Computer Engineering
More informationDigital Signal Processing. Prof. Dietrich Klakow Rahil Mahdian
Digital Signal Processing Prof. Dietrich Klakow Rahil Mahdian Language Teaching: English Questions: English (or German) Slides: English Tutorials: one English and one German group Exercise sheets: most
More informationA Parametric Autoregressive Model for the Extraction of Electric Network Frequency Fluctuations in Audio Forensic Authentication
Journal of Energy and Power Engineering 10 (2016) 504-512 doi: 10.17265/1934-8975/2016.08.007 D DAVID PUBLISHING A Parametric Autoregressive Model for the Extraction of Electric Network Frequency Fluctuations
More informationWHAT MAKES FOR A HIT POP SONG? WHAT MAKES FOR A POP SONG?
WHAT MAKES FOR A HIT POP SONG? WHAT MAKES FOR A POP SONG? NICHOLAS BORG AND GEORGE HOKKANEN Abstract. The possibility of a hit song prediction algorithm is both academically interesting and industry motivated.
More informationComputational Models of Music Similarity. Elias Pampalk National Institute for Advanced Industrial Science and Technology (AIST)
Computational Models of Music Similarity 1 Elias Pampalk National Institute for Advanced Industrial Science and Technology (AIST) Abstract The perceived similarity of two pieces of music is multi-dimensional,
More informationTOWARD UNDERSTANDING EXPRESSIVE PERCUSSION THROUGH CONTENT BASED ANALYSIS
TOWARD UNDERSTANDING EXPRESSIVE PERCUSSION THROUGH CONTENT BASED ANALYSIS Matthew Prockup, Erik M. Schmidt, Jeffrey Scott, and Youngmoo E. Kim Music and Entertainment Technology Laboratory (MET-lab) Electrical
More informationWE ADDRESS the development of a novel computational
IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 18, NO. 3, MARCH 2010 663 Dynamic Spectral Envelope Modeling for Timbre Analysis of Musical Instrument Sounds Juan José Burred, Member,
More informationAutomatic Extraction of Popular Music Ringtones Based on Music Structure Analysis
Automatic Extraction of Popular Music Ringtones Based on Music Structure Analysis Fengyan Wu fengyanyy@163.com Shutao Sun stsun@cuc.edu.cn Weiyao Xue Wyxue_std@163.com Abstract Automatic extraction of
More informationSubjective Similarity of Music: Data Collection for Individuality Analysis
Subjective Similarity of Music: Data Collection for Individuality Analysis Shota Kawabuchi and Chiyomi Miyajima and Norihide Kitaoka and Kazuya Takeda Nagoya University, Nagoya, Japan E-mail: shota.kawabuchi@g.sp.m.is.nagoya-u.ac.jp
More informationAutomatic Piano Music Transcription
Automatic Piano Music Transcription Jianyu Fan Qiuhan Wang Xin Li Jianyu.Fan.Gr@dartmouth.edu Qiuhan.Wang.Gr@dartmouth.edu Xi.Li.Gr@dartmouth.edu 1. Introduction Writing down the score while listening
More informationA QUERY BY EXAMPLE MUSIC RETRIEVAL ALGORITHM
A QUER B EAMPLE MUSIC RETRIEVAL ALGORITHM H. HARB AND L. CHEN Maths-Info department, Ecole Centrale de Lyon. 36, av. Guy de Collongue, 69134, Ecully, France, EUROPE E-mail: {hadi.harb, liming.chen}@ec-lyon.fr
More informationBlind Identification of Source Mobile Devices Using VoIP Calls
Blind Identification of Source Mobile Devices Using VoIP Calls Mehdi Jahanirad 1, Ainuddin Wahid Abdul Wahab, Nor Badrul Anuar, Mohd Yamani Idna Idris, and Mohamad Nizam Ayub Faculty of Computer Science
More informationSinger Recognition and Modeling Singer Error
Singer Recognition and Modeling Singer Error Johan Ismael Stanford University jismael@stanford.edu Nicholas McGee Stanford University ndmcgee@stanford.edu 1. Abstract We propose a system for recognizing
More informationMUSICAL INSTRUMENTCLASSIFICATION USING MIRTOOLBOX
MUSICAL INSTRUMENTCLASSIFICATION USING MIRTOOLBOX MS. ASHWINI. R. PATIL M.E. (Digital System),JSPM s JSCOE Pune, India, ashu.rpatil3690@gmail.com PROF.V.M. SARDAR Assistant professor, JSPM s, JSCOE, Pune,
More informationIntroduction to image compression
Introduction to image compression 1997-2015 Josef Pelikán CGG MFF UK Praha pepca@cgg.mff.cuni.cz http://cgg.mff.cuni.cz/~pepca/ Compression 2015 Josef Pelikán, http://cgg.mff.cuni.cz/~pepca 1 / 12 Motivation
More informationClassification of Timbre Similarity
Classification of Timbre Similarity Corey Kereliuk McGill University March 15, 2007 1 / 16 1 Definition of Timbre What Timbre is Not What Timbre is A 2-dimensional Timbre Space 2 3 Considerations Common
More informationNeural Network for Music Instrument Identi cation
Neural Network for Music Instrument Identi cation Zhiwen Zhang(MSE), Hanze Tu(CCRMA), Yuan Li(CCRMA) SUN ID: zhiwen, hanze, yuanli92 Abstract - In the context of music, instrument identi cation would contribute
More informationAn Efficient Low Bit-Rate Video-Coding Algorithm Focusing on Moving Regions
1128 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 11, NO. 10, OCTOBER 2001 An Efficient Low Bit-Rate Video-Coding Algorithm Focusing on Moving Regions Kwok-Wai Wong, Kin-Man Lam,
More informationSome Experiments in Humour Recognition Using the Italian Wikiquote Collection
Some Experiments in Humour Recognition Using the Italian Wikiquote Collection Davide Buscaldi and Paolo Rosso Dpto. de Sistemas Informáticos y Computación (DSIC), Universidad Politécnica de Valencia, Spain
More informationA Parametric Autoregressive Model for the Extraction of Electric Network Frequency Fluctuations in Audio Forensic Authentication
Proceedings of the 3 rd International Conference on Control, Dynamic Systems, and Robotics (CDSR 16) Ottawa, Canada May 9 10, 2016 Paper No. 110 DOI: 10.11159/cdsr16.110 A Parametric Autoregressive Model
More informationGYROPHONE RECOGNIZING SPEECH FROM GYROSCOPE SIGNALS. Yan Michalevsky (1), Gabi Nakibly (2) and Dan Boneh (1)
GYROPHONE RECOGNIZING SPEECH FROM GYROSCOPE SIGNALS Yan Michalevsky (1), Gabi Nakibly (2) and Dan Boneh (1) (1) Stanford University (2) National Research and Simulation Center, Rafael Ltd. 0 MICROPHONE
More informationDINION 5000 AN. Video DINION 5000 AN. Ultra high resolution 960H sensor
Video TVL 960H Ultra high resoltion 960H sensor High Dynamic Range to see bright and dark details simltaneosly Detail enhancement Bilinx commnication for remote set-p and control Easy to install The DINION
More informationAutomatic Music Similarity Assessment and Recommendation. A Thesis. Submitted to the Faculty. Drexel University. Donald Shaul Williamson
Automatic Music Similarity Assessment and Recommendation A Thesis Submitted to the Faculty of Drexel University by Donald Shaul Williamson in partial fulfillment of the requirements for the degree of Master
More informationSupervised Learning in Genre Classification
Supervised Learning in Genre Classification Introduction & Motivation Mohit Rajani and Luke Ekkizogloy {i.mohit,luke.ekkizogloy}@gmail.com Stanford University, CS229: Machine Learning, 2009 Now that music
More informationOptimized Color Based Compression
Optimized Color Based Compression 1 K.P.SONIA FENCY, 2 C.FELSY 1 PG Student, Department Of Computer Science Ponjesly College Of Engineering Nagercoil,Tamilnadu, India 2 Asst. Professor, Department Of Computer
More informationNeural Network Predicating Movie Box Office Performance
Neural Network Predicating Movie Box Office Performance Alex Larson ECE 539 Fall 2013 Abstract The movie industry is a large part of modern day culture. With the rise of websites like Netflix, where people
More informationDINION 5000 AN. Video DINION 5000 AN. Ultra high resolution 960H sensor
Video www.boschsecrity.com TVL 960H Ultra high resoltion 960H sensor High Dynamic Range to see bright and dark details simltaneosly Detail enhancement Bilinx commnication for remote set-p and control Easy
More informationAutomatic Classification of Instrumental Music & Human Voice Using Formant Analysis
Automatic Classification of Instrumental Music & Human Voice Using Formant Analysis I Diksha Raina, II Sangita Chakraborty, III M.R Velankar I,II Dept. of Information Technology, Cummins College of Engineering,
More informationA. Ideal Ratio Mask If there is no RIR, the IRM for time frame t and frequency f can be expressed as [17]: ( IRM(t, f) =
1 Two-Stage Monaural Source Separation in Reverberant Room Environments using Deep Neural Networks Yang Sun, Student Member, IEEE, Wenwu Wang, Senior Member, IEEE, Jonathon Chambers, Fellow, IEEE, and
More informationDeep Neural Networks Scanning for patterns (aka convolutional networks) Bhiksha Raj
Deep Neural Networks Scanning for patterns (aka convolutional networks) Bhiksha Raj 1 Story so far MLPs are universal function approximators Boolean functions, classifiers, and regressions MLPs can be
More informationToward Multi-Modal Music Emotion Classification
Toward Multi-Modal Music Emotion Classification Yi-Hsuan Yang 1, Yu-Ching Lin 1, Heng-Tze Cheng 1, I-Bin Liao 2, Yeh-Chin Ho 2, and Homer H. Chen 1 1 National Taiwan University 2 Telecommunication Laboratories,
More informationFusion for Audio-Visual Laughter Detection
Fusion for Audio-Visual Laughter Detection Boris Reuderink September 13, 7 2 Abstract Laughter is a highly variable signal, and can express a spectrum of emotions. This makes the automatic detection of
More informationVECTOR REPRESENTATION OF EMOTION FLOW FOR POPULAR MUSIC. Chia-Hao Chung and Homer Chen
VECTOR REPRESENTATION OF EMOTION FLOW FOR POPULAR MUSIC Chia-Hao Chung and Homer Chen National Taiwan University Emails: {b99505003, homer}@ntu.edu.tw ABSTRACT The flow of emotion expressed by music through
More informationDrum Sound Identification for Polyphonic Music Using Template Adaptation and Matching Methods
Drum Sound Identification for Polyphonic Music Using Template Adaptation and Matching Methods Kazuyoshi Yoshii, Masataka Goto and Hiroshi G. Okuno Department of Intelligence Science and Technology National
More informationA Survey of Audio-Based Music Classification and Annotation
A Survey of Audio-Based Music Classification and Annotation Zhouyu Fu, Guojun Lu, Kai Ming Ting, and Dengsheng Zhang IEEE Trans. on Multimedia, vol. 13, no. 2, April 2011 presenter: Yin-Tzu Lin ( 阿孜孜 ^.^)
More informationMulti-modal Analysis for Person Type Classification in News Video
Multi-modal Analysis for Person Type Classification in News Video Jun Yang, Alexander G. Hauptmann School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, PA 15213, USA {juny, alex}@cs.cmu.edu,
More informationMusic Information Retrieval for Jazz
Music Information Retrieval for Jazz Dan Ellis Laboratory for Recognition and Organization of Speech and Audio Dept. Electrical Eng., Columbia Univ., NY USA {dpwe,thierry}@ee.columbia.edu http://labrosa.ee.columbia.edu/
More informationVoice Controlled Car System
Voice Controlled Car System 6.111 Project Proposal Ekin Karasan & Driss Hafdi November 3, 2016 1. Overview Voice controlled car systems have been very important in providing the ability to drivers to adjust
More informationA New Compression Scheme for Color-Quantized Images
904 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 12, NO. 10, OCTOBER 2002 A New Compression Scheme for Color-Quantized Images Xin Chen, Sam Kwong, and Ju-fu Feng Abstract An efficient
More informationPredicting Time-Varying Musical Emotion Distributions from Multi-Track Audio
Predicting Time-Varying Musical Emotion Distributions from Multi-Track Audio Jeffrey Scott, Erik M. Schmidt, Matthew Prockup, Brandon Morton, and Youngmoo E. Kim Music and Entertainment Technology Laboratory
More informationINTRA-FRAME WAVELET VIDEO CODING
INTRA-FRAME WAVELET VIDEO CODING Dr. T. Morris, Mr. D. Britch Department of Computation, UMIST, P. O. Box 88, Manchester, M60 1QD, United Kingdom E-mail: t.morris@co.umist.ac.uk dbritch@co.umist.ac.uk
More informationMOVIES constitute a large sector of the entertainment
1618 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 18, NO. 11, NOVEMBER 2008 Audio-Assisted Movie Dialogue Detection Margarita Kotti, Dimitrios Ververidis, Georgios Evangelopoulos,
More informationA Survey on: Sound Source Separation Methods
Volume 3, Issue 11, November-2016, pp. 580-584 ISSN (O): 2349-7084 International Journal of Computer Engineering In Research Trends Available online at: www.ijcert.org A Survey on: Sound Source Separation
More informationInteractive Classification of Sound Objects for Polyphonic Electro-Acoustic Music Annotation
for Polyphonic Electro-Acoustic Music Annotation Sebastien Gulluni 2, Slim Essid 2, Olivier Buisson, and Gaël Richard 2 Institut National de l Audiovisuel, 4 avenue de l Europe 94366 Bry-sur-marne Cedex,
More informationMindMouse. This project is written in C++ and uses the following Libraries: LibSvm, kissfft, BOOST File System, and Emotiv Research Edition SDK.
Andrew Robbins MindMouse Project Description: MindMouse is an application that interfaces the user s mind with the computer s mouse functionality. The hardware that is required for MindMouse is the Emotiv
More informationNormalized Cumulative Spectral Distribution in Music
Normalized Cumulative Spectral Distribution in Music Young-Hwan Song, Hyung-Jun Kwon, and Myung-Jin Bae Abstract As the remedy used music becomes active and meditation effect through the music is verified,
More informationDINION AN H. Video DINION AN Ultra high resolution 960H sensor
Video www.boschsecrity.com 960H Ultra high resoltion 960H sensor High Dynamic Range to see bright and dark details simltaneosly Detail enhancement Bilinx commnication for remote set-p and control Easy
More informationCopy Move Image Forgery Detection Method Using Steerable Pyramid Transform and Texture Descriptor
Copy Move Image Forgery Detection Method Using Steerable Pyramid Transform and Texture Descriptor Ghulam Muhammad 1, Muneer H. Al-Hammadi 1, Muhammad Hussain 2, Anwar M. Mirza 1, and George Bebis 3 1 Dept.
More informationAudio-Based Video Editing with Two-Channel Microphone
Audio-Based Video Editing with Two-Channel Microphone Tetsuya Takiguchi Organization of Advanced Science and Technology Kobe University, Japan takigu@kobe-u.ac.jp Yasuo Ariki Organization of Advanced Science
More informationNon Stationary Signals (Voice) Verification System Using Wavelet Transform
Non Stationary Signals (Voice) Verification System Using Wavelet Transform PPS Subhashini Associate Professor, Department of ECE, RVR & JC College of Engineering, Guntur. Dr.M.Satya Sairam Professor &
More information