gresearch Focus Cognitive Sciences
|
|
- Simon Quinn
- 5 years ago
- Views:
Transcription
1 Learning about Music Cognition by Asking MIR Questions Sebastian Stober August 12, 2016 CogMIR, New York City MLC g Machine Learning in Cognitive Science Lab
2 From MIR to MIIR Music Imagery Information Retrieval = retrieving music information from brain signals Sebastian Stober - CogMIR
3 12 audio stimuli from 8 music pieces 4 songs recorded each with and without lyrics 4 instrumental pieces complete musical phrases length between 6.9s and 16s (mean 10.5) Sebastian Stober - CogMIR
4 Experiment Setup sound booth presentation system feedback video audio events presentation system screen & speakers feedback keyboard markers recording system stimtracker receiver (optical) EEG amp on battery Biosemi ActiveTwo, 64 EEG + 4 EOG 512 Hz MLC g Sebastian Stober - CogMIR
5 The 12 Music Stimuli songs with / without lyrics: meter tempo length (s) 1 Chim Chim Cheree 3/ Take me out to the Ballgame 3/ Jingle Bells 4/ Mary Had a Little Lamb 4/ instrumental pieces: 1 Emperor Waltz 3/ Harry Potter Theme 3/ Imperial March (Star Wars Theme) 4/ Eine Kleine Nachtmusik 4/ Sebastian Stober - CogMIR
6 MIIR Questions audio reconstruction failed (non-sparsity) stimulus identification beat and tempo tracking meter classification lyrics / non-lyrics / instrumental classification Sebastian Stober - CogMIR
7 Stimulus Identification: Pre-Training Method Learning Distinguishing Features using Similarity-Constraint Encoders Sebastian Stober - CogMIR
8 Similarity-Constraint Encoder exploit synchronization between trials expect similar temporal patterns for the same stimulus goal: improve signal-to-noise ratio learn signal filters that lead to distinguishing (temporal) patterns for the different classes Sebastian Stober - CogMIR
9 Similarity-Constraint Encoder motivated by relative constraints used for metric learning: for all paired trials (A,B) + trial C from other class: sim(a,b) > sim(a,c) many combination for (A,B) and C favors features that are representative and allow to distinguish classes Sebastian Stober - CogMIR
10 Similarity-Constraint Encoder (virtual network structure) Input Triplet Feature Extraction (signal filter) Pairwise Similarity (dot product) Prediction (probabilities) Reference Input Encoder Paired Input Trial Encoder Similarity Softmax Other Input Trial Encoder Similarity (shared weights) minimize constraint violations Sebastian Stober - CogMIR
11 Stimulus Identification 12-class single-trial classification Sebastian Stober - CogMIR
12 Nested Cross-Validation 9-fold subject cross-validation train on data from 8 subjects (8x5x12=480 trials), test on remaining subject (1x5x12=60 trials) Pre-Training Supervised Training 5-fold trial block cross-validation 8x4x12=384 training trials from 4 trial blocks, 8x1x12=96 validation trials from remaining trial block train on triplets from 384 training trials select model (early stopping) based on validation triplets (a,b,c) with a from 96 validation trials and b,c from 480 training and validation trials encoder layer (L1) = average over folds 5-fold trial block cross-validation same training/validation splits as in pre-training phase SVC: select best value for C (grid search) based on highest mean validation accuracy train with selected C on 480 training trials Neural Network: select fold model (early stopping) based on highest validation accuracy classifier layer (L2) = average over folds Sebastian Stober - CogMIR
13 Resulting Spatial Filter only use within-subject trial triplets train on 8 of 9 subjects 9 versions (just 1 filter per encoder!) Sebastian Stober - CogMIR
14 Stimulus Classification (9-fold cross-subject validation) Classifier Features Accuracy SVC raw EEG 18.52% SVC raw EEG channel mean 12.41% End-to-end NN raw EEG 16.30% SVC 12-class encoder output 27.22% Neural Net 12-class encoder output 26.67% significant improvement over baseline (McNemar s test with n=540, p < ) Sebastian Stober - CogMIR
15 Stimulus Classification (9-fold cross-subject validation) Chim Chim Cheree (lyrics) Take Me Out to the Ballgame (lyrics) Jingle Bells (lyrics) Mary Had a Little Lamb (lyrics) Chim Chim Cheree Take Me Out to the Ballgame Jingle Bells Mary Had a Little Lamb Emperor Waltz Hedwig s Theme (Harry Potter) Imperial March (Star Wars Theme) Eine Kleine Nachtmusik Sebastian Stober - CogMIR
16 Mean NN Parameters very simple model similar patterns for lyrics / non-lyrics pairs Sebastian Stober - CogMIR
17 Sebastian Stober - CogMIR
18 Sebastian Stober - CogMIR
19 Classifying Imagination using the same pre-training technique hardly above random accuracy most likely due to poor timing / sync using the same pre-trained filter same problem: hard to learn temporal patterns => experiment redesign / different encoder Sebastian Stober - CogMIR
20 Tempo Extraction [ISMIR 16] Sebastian Stober - CogMIR
21 Tempo Extraction [ISMIR 16] (a) (b) Audio Tempo (BPM) 159 BPM Time (seconds) Tempo (BPM) (c) (d) EEG Tempo (BPM) 158 BPM Time (seconds) Tempo (BPM) Sebastian Stober - CogMIR
22 #peaks tempo error (%) (a) Single-trial (b) Fusion I (c) Fusion II nn = 1 δδ (a) (b) (c) Stimulus ID Absolute BPM Error nn = 2 δδ (a) (b) (c) Stimulus ID Absolute BPM Error δδ (a) (b) (c) nn = error tolerance (BPM) Stimulus ID Participant ID Participant ID Absolute BPM Error
23 Meter Classification 3/4 vs. 4/4 Sebastian Stober - CogMIR
24 Meter Classification (9-fold cross-subject validation) Classifier Features Accuracy SVC raw EEG 62.04% SVC raw EEG channel mean 58.52% End-to-end NN raw EEG 60.56% Dummy output of 12-class classifier 59.63% SVC 12-class encoder output 69.44% Neural Net 12-class encoder output 67,77% SVC meter-class encoder output 60.19% Neural Net meter-class encoder output 58.88% Sebastian Stober - CogMIR
25 Meter Classification (9-fold cross-subject validation using spatial filter from stimulus recognition) spatial filter SVC confusion NN confusion NN temporal patterns: 3/4 4/4 time (samples) Sebastian Stober - CogMIR
26 Group Classification lyrics / non-lyrics / instrumental Sebastian Stober - CogMIR
27 Group Classification (9-fold cross-subject validation) Classifier Features Accuracy SVC raw EEG 40.37% SVC raw EEG channel mean 38.70% End-to-end NN raw EEG 37.40% Dummy output of 12-class classifier 38.89% SVC 12-class encoder output 48.88% Neural Net 12-class encoder output 48.88% SVC group-class encoder output 35.37% Neural Net group-class encoder output 34.63% Sebastian Stober - CogMIR
28 Group Classification (9-fold cross-subject validation using spatial filter from stimulus recognition) spatial filter SVC confusion NN confusion NN temporal patterns: 0x 1x 2x time (samples) Sebastian Stober - CogMIR
29 Conclusions Sebastian Stober - CogMIR
30 MIIR Questions audio reconstruction failed (non-sparsity) stimulus identification beat and tempo tracking meter classification lyrics / non-lyrics / instrumental classification Sebastian Stober - CogMIR
31 Proposed MIIR Approach for different music features attempt classification / regression (derived from typical MIR tasks) use similarity-constraint encoder for contrasting i.e. learn features (from data) that are most different for the classes hypothesis-driven encoder design (assumptions about brain activity / features) limits: amount of trials; subject / stimuli bias Sebastian Stober - CogMIR
32 New Questions 1. How can the spatial filter be interpreted? recall: it produces distinguishable waveforms forward modeling (regression) 2. Which cognitive process results in the prominent signal peak at the 3 rd downbeat? => learn more about music cognition Sebastian Stober - CogMIR
33 Thank You! Avital Sternin, Jessica A. Grahn, Adrian M. Owen, Thomas Prätzlich, Meinard Müller contact: code: (update coming!) dataset: Sebastian Stober - CogMIR
Classifying music perception and imagination using EEG
Western University Scholarship@Western Electronic Thesis and Dissertation Repository June 2016 Classifying music perception and imagination using EEG Avital Sternin The University of Western Ontario Supervisor
More informationBRAIN BEATS: TEMPO EXTRACTION FROM EEG DATA
BRAIN BEATS: TEMPO EXTRACTION FROM EEG DATA Sebastian Stober 1 Thomas Prätzlich 2 Meinard Müller 2 1 Research Focus Cognititive Sciences, University of Potsdam, Germany 2 International Audio Laboratories
More informationTOWARDS MUSIC IMAGERY INFORMATION RETRIEVAL: INTRODUCING THE OPENMIIR DATASET OF EEG RECORDINGS FROM MUSIC PERCEPTION AND IMAGINATION
TOWARDS MUSIC IMAGERY INFORMATION RETRIEVAL: INTRODUCING THE OPENMIIR DATASET OF EEG RECORDINGS FROM MUSIC PERCEPTION AND IMAGINATION Sebastian Stober, Avital Sternin, Adrian M. Owen and Jessica A. Grahn
More informationBrain-Computer Interface (BCI)
Brain-Computer Interface (BCI) Christoph Guger, Günter Edlinger, g.tec Guger Technologies OEG Herbersteinstr. 60, 8020 Graz, Austria, guger@gtec.at This tutorial shows HOW-TO find and extract proper signal
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 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 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 informationDATA! NOW WHAT? Preparing your ERP data for analysis
DATA! NOW WHAT? Preparing your ERP data for analysis Dennis L. Molfese, Ph.D. Caitlin M. Hudac, B.A. Developmental Brain Lab University of Nebraska-Lincoln 1 Agenda Pre-processing Preparing for analysis
More informationAudio. Meinard Müller. Beethoven, Bach, and Billions of Bytes. International Audio Laboratories Erlangen. International Audio Laboratories Erlangen
Meinard Müller Beethoven, Bach, and Billions of Bytes When Music meets Computer Science Meinard Müller International Laboratories Erlangen meinard.mueller@audiolabs-erlangen.de School of Mathematics University
More informationMusic Emotion Recognition. Jaesung Lee. Chung-Ang University
Music Emotion Recognition Jaesung Lee Chung-Ang University Introduction Searching Music in Music Information Retrieval Some information about target music is available Query by Text: Title, Artist, or
More informationThe Million Song Dataset
The Million Song Dataset AUDIO FEATURES The Million Song Dataset There is no data like more data Bob Mercer of IBM (1985). T. Bertin-Mahieux, D.P.W. Ellis, B. Whitman, P. Lamere, The Million Song Dataset,
More informationBeethoven, Bach, and Billions of Bytes
Lecture Music Processing Beethoven, Bach, and Billions of Bytes New Alliances between Music and Computer Science Meinard Müller International Audio Laboratories Erlangen meinard.mueller@audiolabs-erlangen.de
More informationMusic Representations. Beethoven, Bach, and Billions of Bytes. Music. Research Goals. Piano Roll Representation. Player Piano (1900)
Music Representations Lecture Music Processing Sheet Music (Image) CD / MP3 (Audio) MusicXML (Text) Beethoven, Bach, and Billions of Bytes New Alliances between Music and Computer Science Dance / Motion
More informationDAT335 Music Perception and Cognition Cogswell Polytechnical College Spring Week 6 Class Notes
DAT335 Music Perception and Cognition Cogswell Polytechnical College Spring 2009 Week 6 Class Notes Pitch Perception Introduction Pitch may be described as that attribute of auditory sensation in terms
More informationMusic Synchronization. Music Synchronization. Music Data. Music Data. General Goals. Music Information Retrieval (MIR)
Advanced Course Computer Science Music Processing Summer Term 2010 Music ata Meinard Müller Saarland University and MPI Informatik meinard@mpi-inf.mpg.de Music Synchronization Music ata Various interpretations
More informationAutomatic Music Clustering using Audio Attributes
Automatic Music Clustering using Audio Attributes Abhishek Sen BTech (Electronics) Veermata Jijabai Technological Institute (VJTI), Mumbai, India abhishekpsen@gmail.com Abstract Music brings people together,
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 informationMusic Information Retrieval
Music Information Retrieval When Music Meets Computer Science Meinard Müller International Audio Laboratories Erlangen meinard.mueller@audiolabs-erlangen.de Berlin MIR Meetup 20.03.2017 Meinard Müller
More informationBeethoven, Bach und Billionen Bytes
Meinard Müller Beethoven, Bach und Billionen Bytes Automatisierte Analyse von Musik und Klängen Meinard Müller Lehrerfortbildung in Informatik Dagstuhl, Dezember 2014 2001 PhD, Bonn University 2002/2003
More informationPre-Processing of ERP Data. Peter J. Molfese, Ph.D. Yale University
Pre-Processing of ERP Data Peter J. Molfese, Ph.D. Yale University Before Statistical Analyses, Pre-Process the ERP data Planning Analyses Waveform Tools Types of Tools Filter Segmentation Visual Review
More informationSkip Length and Inter-Starvation Distance as a Combined Metric to Assess the Quality of Transmitted Video
Skip Length and Inter-Starvation Distance as a Combined Metric to Assess the Quality of Transmitted Video Mohamed Hassan, Taha Landolsi, Husameldin Mukhtar, and Tamer Shanableh College of Engineering American
More informationData-Driven Solo Voice Enhancement for Jazz Music Retrieval
Data-Driven Solo Voice Enhancement for Jazz Music Retrieval Stefan Balke1, Christian Dittmar1, Jakob Abeßer2, Meinard Müller1 1International Audio Laboratories Erlangen 2Fraunhofer Institute for Digital
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 informationLecture 9 Source Separation
10420CS 573100 音樂資訊檢索 Music Information Retrieval Lecture 9 Source Separation Yi-Hsuan Yang Ph.D. http://www.citi.sinica.edu.tw/pages/yang/ yang@citi.sinica.edu.tw Music & Audio Computing Lab, Research
More informationAbout Giovanni De Poli. What is Model. Introduction. di Poli: Methodologies for Expressive Modeling of/for Music Performance
Methodologies for Expressiveness Modeling of and for Music Performance by Giovanni De Poli Center of Computational Sonology, Department of Information Engineering, University of Padova, Padova, Italy About
More informationPredicting the immediate future with Recurrent Neural Networks: Pre-training and Applications
Predicting the immediate future with Recurrent Neural Networks: Pre-training and Applications Introduction Brandon Richardson December 16, 2011 Research preformed from the last 5 years has shown that the
More informationMusic Composition with RNN
Music Composition with RNN Jason Wang Department of Statistics Stanford University zwang01@stanford.edu Abstract Music composition is an interesting problem that tests the creativity capacities of artificial
More informationFeature-Based Analysis of Haydn String Quartets
Feature-Based Analysis of Haydn String Quartets Lawson Wong 5/5/2 Introduction When listening to multi-movement works, amateur listeners have almost certainly asked the following situation : Am I still
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 informationMusic Similarity and Cover Song Identification: The Case of Jazz
Music Similarity and Cover Song Identification: The Case of Jazz Simon Dixon and Peter Foster s.e.dixon@qmul.ac.uk Centre for Digital Music School of Electronic Engineering and Computer Science Queen Mary
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 informationExperiment PP-1: Electroencephalogram (EEG) Activity
Experiment PP-1: Electroencephalogram (EEG) Activity Exercise 1: Common EEG Artifacts Aim: To learn how to record an EEG and to become familiar with identifying EEG artifacts, especially those related
More informationTempo and Beat Tracking
Tutorial Automatisierte Methoden der Musikverarbeitung 47. Jahrestagung der Gesellschaft für Informatik Tempo and Beat Tracking Meinard Müller, Christof Weiss, Stefan Balke International Audio Laboratories
More informationMusic Processing Introduction Meinard Müller
Lecture Music Processing Introduction Meinard Müller International Audio Laboratories Erlangen meinard.mueller@audiolabs-erlangen.de Music Music Information Retrieval (MIR) Sheet Music (Image) CD / MP3
More informationCommon Spatial Patterns 3 class BCI V Copyright 2012 g.tec medical engineering GmbH
g.tec medical engineering GmbH Sierningstrasse 14, A-4521 Schiedlberg Austria - Europe Tel.: (43)-7251-22240-0 Fax: (43)-7251-22240-39 office@gtec.at, http://www.gtec.at Common Spatial Patterns 3 class
More informationOBJECTIVE EVALUATION OF A MELODY EXTRACTOR FOR NORTH INDIAN CLASSICAL VOCAL PERFORMANCES
OBJECTIVE EVALUATION OF A MELODY EXTRACTOR FOR NORTH INDIAN CLASSICAL VOCAL PERFORMANCES Vishweshwara Rao and Preeti Rao Digital Audio Processing Lab, Electrical Engineering Department, IIT-Bombay, Powai,
More informationMelody Extraction from Generic Audio Clips Thaminda Edirisooriya, Hansohl Kim, Connie Zeng
Melody Extraction from Generic Audio Clips Thaminda Edirisooriya, Hansohl Kim, Connie Zeng Introduction In this project we were interested in extracting the melody from generic audio files. Due to the
More informationMeinard Müller. Beethoven, Bach, und Billionen Bytes. International Audio Laboratories Erlangen. International Audio Laboratories Erlangen
Beethoven, Bach, und Billionen Bytes Musik trifft Informatik Meinard Müller Meinard Müller 2007 Habilitation, Bonn 2007 MPI Informatik, Saarbrücken Senior Researcher Music Processing & Motion Processing
More informationTake a Break, Bach! Let Machine Learning Harmonize That Chorale For You. Chris Lewis Stanford University
Take a Break, Bach! Let Machine Learning Harmonize That Chorale For You Chris Lewis Stanford University cmslewis@stanford.edu Abstract In this project, I explore the effectiveness of the Naive Bayes Classifier
More informationExperimenting with Musically Motivated Convolutional Neural Networks
Experimenting with Musically Motivated Convolutional Neural Networks Jordi Pons 1, Thomas Lidy 2 and Xavier Serra 1 1 Music Technology Group, Universitat Pompeu Fabra, Barcelona 2 Institute of Software
More informationData Driven Music Understanding
Data Driven Music Understanding Dan Ellis Laboratory for Recognition and Organization of Speech and Audio Dept. Electrical Engineering, Columbia University, NY USA http://labrosa.ee.columbia.edu/ 1. Motivation:
More informationCommon Spatial Patterns 2 class BCI V Copyright 2012 g.tec medical engineering GmbH
g.tec medical engineering GmbH Sierningstrasse 14, A-4521 Schiedlberg Austria - Europe Tel.: (43)-7251-22240-0 Fax: (43)-7251-22240-39 office@gtec.at, http://www.gtec.at Common Spatial Patterns 2 class
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 informationTempo and Beat Analysis
Advanced Course Computer Science Music Processing Summer Term 2010 Meinard Müller, Peter Grosche Saarland University and MPI Informatik meinard@mpi-inf.mpg.de Tempo and Beat Analysis Musical Properties:
More informationSupplemental Material for Gamma-band Synchronization in the Macaque Hippocampus and Memory Formation
Supplemental Material for Gamma-band Synchronization in the Macaque Hippocampus and Memory Formation Michael J. Jutras, Pascal Fries, Elizabeth A. Buffalo * *To whom correspondence should be addressed.
More informationEnabling editors through machine learning
Meta Follow Meta is an AI company that provides academics & innovation-driven companies with powerful views of t Dec 9, 2016 9 min read Enabling editors through machine learning Examining the data science
More informationWHAT'S HOT: LINEAR POPULARITY PREDICTION FROM TV AND SOCIAL USAGE DATA Jan Neumann, Xiaodong Yu, and Mohamad Ali Torkamani Comcast Labs
WHAT'S HOT: LINEAR POPULARITY PREDICTION FROM TV AND SOCIAL USAGE DATA Jan Neumann, Xiaodong Yu, and Mohamad Ali Torkamani Comcast Labs Abstract Large numbers of TV channels are available to TV consumers
More informationTRACKING THE ODD : METER INFERENCE IN A CULTURALLY DIVERSE MUSIC CORPUS
TRACKING THE ODD : METER INFERENCE IN A CULTURALLY DIVERSE MUSIC CORPUS Andre Holzapfel New York University Abu Dhabi andre@rhythmos.org Florian Krebs Johannes Kepler University Florian.Krebs@jku.at Ajay
More informationReconstruction of Ca 2+ dynamics from low frame rate Ca 2+ imaging data CS229 final project. Submitted by: Limor Bursztyn
Reconstruction of Ca 2+ dynamics from low frame rate Ca 2+ imaging data CS229 final project. Submitted by: Limor Bursztyn Introduction Active neurons communicate by action potential firing (spikes), accompanied
More informationMusic BCI ( )
Music BCI (006-2015) Matthias Treder, Benjamin Blankertz Technische Universität Berlin, Berlin, Germany September 5, 2016 1 Introduction We investigated the suitability of musical stimuli for use in a
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 informationObject selectivity of local field potentials and spikes in the macaque inferior temporal cortex
Object selectivity of local field potentials and spikes in the macaque inferior temporal cortex Gabriel Kreiman 1,2,3,4*#, Chou P. Hung 1,2,4*, Alexander Kraskov 5, Rodrigo Quian Quiroga 6, Tomaso Poggio
More informationFIBRE CHANNEL CONSORTIUM
FIBRE CHANNEL CONSORTIUM FC-PI-2 Clause 6 Optical Physical Layer Test Suite Version 0.51 Technical Document Last Updated: August 15, 2005 Fibre Channel Consortium Durham, NH 03824 Phone: +1-603-862-0701
More informationDeepID: Deep Learning for Face Recognition. Department of Electronic Engineering,
DeepID: Deep Learning for Face Recognition Xiaogang Wang Department of Electronic Engineering, The Chinese University i of Hong Kong Machine Learning with Big Data Machine learning with small data: overfitting,
More informationImage-to-Markup Generation with Coarse-to-Fine Attention
Image-to-Markup Generation with Coarse-to-Fine Attention Presenter: Ceyer Wakilpoor Yuntian Deng 1 Anssi Kanervisto 2 Alexander M. Rush 1 Harvard University 3 University of Eastern Finland ICML, 2017 Yuntian
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 informationMusic Information Retrieval (MIR)
Ringvorlesung Perspektiven der Informatik Wintersemester 2011/2012 Meinard Müller Universität des Saarlandes und MPI Informatik meinard@mpi-inf.mpg.de Priv.-Doz. Dr. Meinard Müller 2007 Habilitation, Bonn
More informationSmooth Rhythms as Probes of Entrainment. Music Perception 10 (1993): ABSTRACT
Smooth Rhythms as Probes of Entrainment Music Perception 10 (1993): 503-508 ABSTRACT If one hypothesizes rhythmic perception as a process employing oscillatory circuits in the brain that entrain to low-frequency
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 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 informationStructured training for large-vocabulary chord recognition. Brian McFee* & Juan Pablo Bello
Structured training for large-vocabulary chord recognition Brian McFee* & Juan Pablo Bello Small chord vocabularies Typically a supervised learning problem N C:maj C:min C#:maj C#:min D:maj D:min......
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 informationGood playing practice when drumming: Influence of tempo on timing and preparatory movements for healthy and dystonic players
International Symposium on Performance Science ISBN 978-94-90306-02-1 The Author 2011, Published by the AEC All rights reserved Good playing practice when drumming: Influence of tempo on timing and preparatory
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 informationJOINT BEAT AND DOWNBEAT TRACKING WITH RECURRENT NEURAL NETWORKS
JOINT BEAT AND DOWNBEAT TRACKING WITH RECURRENT NEURAL NETWORKS Sebastian Böck, Florian Krebs, and Gerhard Widmer Department of Computational Perception Johannes Kepler University Linz, Austria sebastian.boeck@jku.at
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 informationHidden Markov Model based dance recognition
Hidden Markov Model based dance recognition Dragutin Hrenek, Nenad Mikša, Robert Perica, Pavle Prentašić and Boris Trubić University of Zagreb, Faculty of Electrical Engineering and Computing Unska 3,
More informationVideo-based Vibrato Detection and Analysis for Polyphonic String Music
Video-based Vibrato Detection and Analysis for Polyphonic String Music Bochen Li, Karthik Dinesh, Gaurav Sharma, Zhiyao Duan Audio Information Research Lab University of Rochester The 18 th International
More informationAPPLICATIONS OF A SEMI-AUTOMATIC MELODY EXTRACTION INTERFACE FOR INDIAN MUSIC
APPLICATIONS OF A SEMI-AUTOMATIC MELODY EXTRACTION INTERFACE FOR INDIAN MUSIC Vishweshwara Rao, Sachin Pant, Madhumita Bhaskar and Preeti Rao Department of Electrical Engineering, IIT Bombay {vishu, sachinp,
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 informationFitt s Law Study Report Amia Oberai
Fitt s Law Study Report Amia Oberai Overview of the study The aim of this study was to investigate the effect of different music genres and tempos on people s pointing interactions. 5 participants took
More informationBitWise (V2.1 and later) includes features for determining AP240 settings and measuring the Single Ion Area.
BitWise. Instructions for New Features in ToF-AMS DAQ V2.1 Prepared by Joel Kimmel University of Colorado at Boulder & Aerodyne Research Inc. Last Revised 15-Jun-07 BitWise (V2.1 and later) includes features
More informationMusic Genre Classification
Music Genre Classification chunya25 Fall 2017 1 Introduction A genre is defined as a category of artistic composition, characterized by similarities in form, style, or subject matter. [1] Some researchers
More informationTemplate Matching for Artifact Detection and Removal
RADBOUD UNIVERSITY NIJMEGEN Template Matching for Artifact Detection and Removal by R.Barth supervised by prof. dr. ir. P.Desain and drs. R. Vlek A thesis submitted in partial fulfillment for the degree
More informationDr Kelly Jakubowski Music Psychologist October 2017
Dr Kelly Jakubowski Music Psychologist October 2017 Overview Musical rhythm: Introduction Rhythm and movement Rhythm and language Rhythm and social engagement Introduction Engaging with music can teach
More informationarxiv: v1 [cs.lg] 15 Jun 2016
Deep Learning for Music arxiv:1606.04930v1 [cs.lg] 15 Jun 2016 Allen Huang Department of Management Science and Engineering Stanford University allenh@cs.stanford.edu Abstract Raymond Wu Department of
More informationFurther Topics in MIR
Tutorial Automatisierte Methoden der Musikverarbeitung 47. Jahrestagung der Gesellschaft für Informatik Further Topics in MIR Meinard Müller, Christof Weiss, Stefan Balke International Audio Laboratories
More informationProjektseminar: Sentimentanalyse Dozenten: Michael Wiegand und Marc Schulder
Projektseminar: Sentimentanalyse Dozenten: Michael Wiegand und Marc Schulder Präsentation des Papers ICWSM A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews
More informationJoint bottom-up/top-down machine learning structures to simulate human audition and musical creativity
Joint bottom-up/top-down machine learning structures to simulate human audition and musical creativity Jonas Braasch Director of Operations, Professor, School of Architecture Rensselaer Polytechnic Institute,
More informationTHE INTERACTION BETWEEN MELODIC PITCH CONTENT AND RHYTHMIC PERCEPTION. Gideon Broshy, Leah Latterner and Kevin Sherwin
THE INTERACTION BETWEEN MELODIC PITCH CONTENT AND RHYTHMIC PERCEPTION. BACKGROUND AND AIMS [Leah Latterner]. Introduction Gideon Broshy, Leah Latterner and Kevin Sherwin Yale University, Cognition of Musical
More informationDAY 1. Intelligent Audio Systems: A review of the foundations and applications of semantic audio analysis and music information retrieval
DAY 1 Intelligent Audio Systems: A review of the foundations and applications of semantic audio analysis and music information retrieval Jay LeBoeuf Imagine Research jay{at}imagine-research.com Rebecca
More informationAn ecological approach to multimodal subjective music similarity perception
An ecological approach to multimodal subjective music similarity perception Stephan Baumann German Research Center for AI, Germany www.dfki.uni-kl.de/~baumann John Halloran Interact Lab, Department of
More informationPersonalized TV Recommendation with Mixture Probabilistic Matrix Factorization
Personalized TV Recommendation with Mixture Probabilistic Matrix Factorization Huayu Li, Hengshu Zhu #, Yong Ge, Yanjie Fu +,Yuan Ge Computer Science Department, UNC Charlotte # Baidu Research-Big Data
More informationAN ARTISTIC TECHNIQUE FOR AUDIO-TO-VIDEO TRANSLATION ON A MUSIC PERCEPTION STUDY
AN ARTISTIC TECHNIQUE FOR AUDIO-TO-VIDEO TRANSLATION ON A MUSIC PERCEPTION STUDY Eugene Mikyung Kim Department of Music Technology, Korea National University of Arts eugene@u.northwestern.edu ABSTRACT
More informationMusic Information Retrieval (MIR)
Ringvorlesung Perspektiven der Informatik Sommersemester 2010 Meinard Müller Universität des Saarlandes und MPI Informatik meinard@mpi-inf.mpg.de Priv.-Doz. Dr. Meinard Müller 2007 Habilitation, Bonn 2007
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 informationSarcasm Detection in Text: Design Document
CSC 59866 Senior Design Project Specification Professor Jie Wei Wednesday, November 23, 2016 Sarcasm Detection in Text: Design Document Jesse Feinman, James Kasakyan, Jeff Stolzenberg 1 Table of contents
More informationMeasurements on GSM Base Stations According to Rec
Measurements on GSM Base Stations According to Rec. 11.20 Application Note 1EF23_0L Subject to change 10 September 96, Josef Wolf / Roland Minihold Products: FSE incl. Option FSE-B7 1 Introduction The
More informationAutomatic Construction of Synthetic Musical Instruments and Performers
Ph.D. Thesis Proposal Automatic Construction of Synthetic Musical Instruments and Performers Ning Hu Carnegie Mellon University Thesis Committee Roger B. Dannenberg, Chair Michael S. Lewicki Richard M.
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 informationOutline. Why do we classify? Audio Classification
Outline Introduction Music Information Retrieval Classification Process Steps Pitch Histograms Multiple Pitch Detection Algorithm Musical Genre Classification Implementation Future Work Why do we classify
More informationarxiv: v1 [cs.ir] 16 Jan 2019
It s Only Words And Words Are All I Have Manash Pratim Barman 1, Kavish Dahekar 2, Abhinav Anshuman 3, and Amit Awekar 4 1 Indian Institute of Information Technology, Guwahati 2 SAP Labs, Bengaluru 3 Dell
More informationDESIGNING OPTIMIZED MICROPHONE BEAMFORMERS
3235 Kifer Rd. Suite 100 Santa Clara, CA 95051 www.dspconcepts.com DESIGNING OPTIMIZED MICROPHONE BEAMFORMERS Our previous paper, Fundamentals of Voice UI, explained the algorithms and processes required
More informationMusic Representations
Lecture Music Processing Music Representations Meinard Müller International Audio Laboratories Erlangen meinard.mueller@audiolabs-erlangen.de Book: Fundamentals of Music Processing Meinard Müller Fundamentals
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 informationONE SENSOR MICROPHONE ARRAY APPLICATION IN SOURCE LOCALIZATION. Hsin-Chu, Taiwan
ICSV14 Cairns Australia 9-12 July, 2007 ONE SENSOR MICROPHONE ARRAY APPLICATION IN SOURCE LOCALIZATION Percy F. Wang 1 and Mingsian R. Bai 2 1 Southern Research Institute/University of Alabama at Birmingham
More informationinter.noise 2000 The 29th International Congress and Exhibition on Noise Control Engineering August 2000, Nice, FRANCE
Copyright SFA - InterNoise 2000 1 inter.noise 2000 The 29th International Congress and Exhibition on Noise Control Engineering 27-30 August 2000, Nice, FRANCE I-INCE Classification: 7.9 THE FUTURE OF SOUND
More informationFinger motion in piano performance: Touch and tempo
International Symposium on Performance Science ISBN 978-94-936--4 The Author 9, Published by the AEC All rights reserved Finger motion in piano performance: Touch and tempo Werner Goebl and Caroline Palmer
More informationClassification of Dance Music by Periodicity Patterns
Classification of Dance Music by Periodicity Patterns Simon Dixon Austrian Research Institute for AI Freyung 6/6, Vienna 1010, Austria simon@oefai.at Elias Pampalk Austrian Research Institute for AI Freyung
More informationPitch Perception. Roger Shepard
Pitch Perception Roger Shepard Pitch Perception Ecological signals are complex not simple sine tones and not always periodic. Just noticeable difference (Fechner) JND, is the minimal physical change detectable
More information