Music Structure Analysis
|
|
- Lynne Welch
- 5 years ago
- Views:
Transcription
1 Lecture Music Processing Music Structure Analysis Meinard Müller International Audio Laboratories Erlangen
2 Book: Fundamentals of Music Processing Meinard Müller Fundamentals of Music Processing Audio, Analysis, Algorithms, Applications 483 p., 249 illus., hardcover ISBN: Springer, 2015 Accompanying website:
3 Book: Fundamentals of Music Processing Meinard Müller Fundamentals of Music Processing Audio, Analysis, Algorithms, Applications 483 p., 249 illus., hardcover ISBN: Springer, 2015 Accompanying website:
4 Book: Fundamentals of Music Processing Meinard Müller Fundamentals of Music Processing Audio, Analysis, Algorithms, Applications 483 p., 249 illus., hardcover ISBN: Springer, 2015 Accompanying website:
5 Chapter 4: Music Structure Analysis 4.1 General Principles 4.2 Self-Similarity Matrices 4.3 Audio Thumbnailing 4.4 Novelty-Based Segmentation 4.5 Evaluation 4.6 Further Notes In Chapter 4, we address a central and well-researched area within MIR known as music structure analysis. Given a music recording, the objective is to identify important structural elements and to temporally segment the recording according to these elements. Within this scenario, we discuss fundamental segmentation principles based on repetitions, homogeneity, and novelty principles that also apply to other types of multimedia beyond music. As an important technical tool, we study in detail the concept of self-similarity matrices and discuss their structural properties. Finally, we briefly touch the topic of evaluation, introducing the notions of precision, recall, and F-measure.
6 Music Structure Analysis Example: Zager & Evans In The Year 2525 Time (seconds)
7 Music Structure Analysis Example: Zager & Evans In The Year 2525 Time (seconds)
8 Music Structure Analysis Example: Zager & Evans In The Year 2525 I V1 V2 V3 V4 V5 V6 V7 B V8 O
9 Music Structure Analysis Example: Brahms Hungarian Dance No. 5 (Ormandy) A1 A2 B1 B2 C A3 B3 B4 Time (seconds)
10 Music Structure Analysis Example: Folk Song Field Recording (Nederlandse Liederenbank) Time (seconds)
11 Music Structure Analysis Example: Weber, Song (No. 4) from Der Freischütz Introduction Stanzas Dialogues Kleiber Time (seconds) Ackermann Time (seconds)
12 Music Structure Analysis General goal: Divide an audio recording into temporal segments corresponding to musical parts and group these segments into musically meaningful categories. Examples: Stanzas of a folk song Intro, verse, chorus, bridge, outro sections of a pop song Exposition, development, recapitulation, coda of a sonata Musical form ABACADA of a rondo
13 Music Structure Analysis General goal: Divide an audio recording into temporal segments corresponding to musical parts and group these segments into musically meaningful categories. Challenge: There are many different principles for creating relationships that form the basis for the musical structure. Homogeneity: Novelty: Repetition: Consistency in tempo, instrumentation, key, Sudden changes, surprising elements Repeating themes, motives, rhythmic patterns,
14 Music Structure Analysis Novelty Homogeneity Repetition
15 Overview Introduction Feature Representations Self-Similarity Matrices Audio Thumbnailing Novelty-based Segmentation Thanks: Clausen, Ewert, Kurth, Grohganz, Dannenberg, Goto Grosche, Jiang Paulus, Klapuri Peeters, Kaiser, Serra, Gómez, Smith, Fujinaga, Wiering, Wand, Sunkel, Jansen
16 Overview Introduction Feature Representations Self-Similarity Matrices Audio Thumbnailing Novelty-based Segmentation Thanks: Clausen, Ewert, Kurth, Grohganz, Dannenberg, Goto Grosche, Jiang Paulus, Klapuri Peeters, Kaiser, Serra, Gómez, Smith, Fujinaga, Wiering, Wand, Sunkel, Jansen
17 Feature Representation General goal: Convert an audio recording into a mid-level representation that captures certain musical properties while supressing other properties. Timbre / Instrumentation Tempo / Rhythm Pitch / Harmony
18 Feature Representation General goal: Convert an audio recording into a mid-level representation that captures certain musical properties while supressing other properties. Timbre / Instrumentation Tempo / Rhythm Pitch / Harmony
19 Feature Representation Example: Chromatic scale C1 24 C2 36 C3 48 C4 60 C5 72 C6 84 C7 96 C8 108 Waveform Amplitude Time (seconds)
20 Feature Representation Frequency (Hz) Frequency (Hz) Intensity (db) Intensity (db) Example: Chromatic scale C1 24 Spectrogram C2 36 C3 48 C4 60 C5 72 Time (seconds) C6 84 C7 96 C8 108
21 Feature Representation Frequency (Hz) Frequency (Hz) Intensity (db) Intensity (db) Example: Chromatic scale C1 24 Spectrogram C2 36 C3 48 C4 60 C5 72 Time (seconds) C6 84 C7 96 C8 108
22 Feature Representation Example: Chromatic scale C1 24 Spectrogram C2 36 C3 48 C4 60 C5 72 C6 84 C7 96 C8 108 C8: 4186 Hz C7: 2093 Hz C6: 1046 Hz C5: 523 Hz C4: 261 Hz C3: 131 Hz Time (seconds) Intensity (db)
23 Feature Representation Example: Chromatic scale C1 24 Log-frequency spectrogram C2 36 C3 48 C4 60 C5 72 C6 84 C7 96 C8 108 C8: 4186 Hz C7: 2093 Hz C6: 1046 Hz C5: 523 Hz C4: 261 Hz C3: 131 Hz Intensity (db) Time (seconds)
24 Feature Representation Example: Chromatic scale C1 24 Log-frequency spectrogram C2 36 C3 48 C4 60 C5 72 C6 84 C7 96 C8 108 Pitch (MIDI note number) Intensity (db) Time (seconds)
25 Feature Representation Example: Chromatic scale C1 24 Log-frequency spectrogram C2 36 C3 48 C4 60 C5 72 C6 84 C7 96 C8 108 Pitch (MIDI note number) Intensity (db) Chroma C Time (seconds)
26 Feature Representation Example: Chromatic scale C1 24 Log-frequency spectrogram C2 36 C3 48 C4 60 C5 72 C6 84 C7 96 C8 108 Pitch (MIDI note number) Intensity (db) Chroma C # Time (seconds)
27 Feature Representation Example: Chromatic scale C1 24 C2 36 C3 48 C4 60 C5 72 C6 84 C7 96 C8 108 Chroma representation Chroma Intensity (db) Time (seconds)
28 Feature Representation Example: Brahms Hungarian Dance No. 5 (Ormandy) A1 A2 B1 B2 C A3 B3 B4 Time (seconds)
29 Feature Representation Example: Brahms Hungarian Dance No. 5 (Ormandy) Chroma (Harmony) Feature extraction A1 A2 B1 B2 C A3 B3 B4 Time (seconds)
30 Feature Representation Example: Brahms Hungarian Dance No. 5 (Ormandy) B b G D Chroma (Harmony) G minor Feature extraction G minor A1 A2 B1 B2 C A3 B3 B4 Time (seconds)
31 Feature Representation Example: Brahms Hungarian Dance No. 5 (Ormandy) B G B b G Chroma (Harmony) Feature extraction D D G minor G major G minor A1 A2 B1 B2 C A3 B3 B4 Time (seconds)
32 Overview Introduction Feature Representations Self-Similarity Matrices Audio Thumbnailing Novelty-based Segmentation
33 Self-Similarity Matrix (SSM) General idea: Compare each element of the feature sequence with each other element of the feature sequence based on a suitable similarity measure. Quadratic self-similarity matrix
34 Self-Similarity Matrix (SSM) Example: Brahms Hungarian Dance No. 5 (Ormandy)
35 Self-Similarity Matrix (SSM) Example: Brahms Hungarian Dance No. 5 (Ormandy)
36 Self-Similarity Matrix (SSM) Example: Brahms Hungarian Dance No. 5 (Ormandy)
37 Self-Similarity Matrix (SSM) Example: Brahms Hungarian Dance No. 5 (Ormandy)
38 Self-Similarity Matrix (SSM) Example: Brahms Hungarian Dance No. 5 (Ormandy)
39 Self-Similarity Matrix (SSM) Example: Brahms Hungarian Dance No. 5 (Ormandy)
40 Self-Similarity Matrix (SSM) Example: Brahms Hungarian Dance No. 5 (Ormandy) G major G major
41 Self-Similarity Matrix (SSM) Example: Brahms Hungarian Dance No. 5 (Ormandy)
42 Self-Similarity Matrix (SSM) Example: Brahms Hungarian Dance No. 5 (Ormandy)
43 Self-Similarity Matrix (SSM) Example: Brahms Hungarian Dance No. 5 (Ormandy)
44 Self-Similarity Matrix (SSM) Example: Brahms Hungarian Dance No. 5 (Ormandy)
45 Self-Similarity Matrix (SSM) Example: Brahms Hungarian Dance No. 5 (Ormandy) Faster Slower
46 Self-Similarity Matrix (SSM) Example: Brahms Hungarian Dance No. 5 (Ormandy) Faster Slower
47 Self-Similarity Matrix (SSM) Example: Brahms Hungarian Dance No. 5 (Ormandy) Idealized SSM
48 Self-Similarity Matrix (SSM) Example: Brahms Hungarian Dance No. 5 (Ormandy) Idealized SSM Blocks: Homogeneity Paths: Repetition Corners: Novelty
49 SSM Enhancement Block Enhancement Feature smoothing Coarsening Time (samples) Time (samples)
50 SSM Enhancement Block Enhancement Feature smoothing Coarsening Time (samples) Time (samples)
51 SSM Enhancement Block Enhancement Feature smoothing Coarsening Time (samples) Time (samples)
52 SSM Enhancement Challenge: Presence of musical variations Fragmented paths and gaps Paths of poor quality Regions of constant (high) similarity Curved paths Idea: Enhancement of path structure
53 SSM Enhancement Shostakovich Waltz 2, Jazz Suite No. 2 (Chailly) SSM
54 SSM Enhancement Shostakovich Waltz 2, Jazz Suite No. 2 (Chailly) SSM
55 SSM Enhancement Shostakovich Waltz 2, Jazz Suite No. 2 (Chailly) SSM
56 SSM Enhancement Shostakovich Waltz 2, Jazz Suite No. 2 (Chailly) Enhanced SSM Filtering along main diagonal
57 SSM Enhancement Idea: Usage of contextual information (Foote 1999) Comparison of entire sequences = length of sequences = enhanced SSM smoothing effect
58 SSM Enhancement SSM
59 SSM Enhancement Enhanced SSM with Filtering along main diagonal
60 SSM Enhancement Enhanced SSM with Filtering along 8 different directions and minimizing
61 SSM Enhancement Idea: Smoothing along various directions and minimizing over all directions Tempo changes of -50 to +50 percent
62 SSM Enhancement Path Enhancement Time (samples) Time (samples)
63 SSM Enhancement Path Enhancement Diagonal smoothing Time (samples) Time (samples)
64 SSM Enhancement Path Enhancement Diagonal smoothing Multiple filtering Time (samples) Time (samples)
65 SSM Enhancement Path Enhancement Diagonal smoothing Multiple filtering Thresholding (relative) Scaling & penalty Time (samples) Time (samples)
66 SSM Enhancement Further Processing Path extraction Time (samples) Time (samples)
67 SSM Enhancement Further Processing Path extraction Pairwise relations Time (samples) Time (samples) Time (samples)
68 SSM Enhancement Further Processing Path extraction Pairwise relations Grouping (transitivity) Time (samples) Time (samples) Time (samples)
69 SSM Enhancement Further Processing Path extraction Pairwise relations Grouping (transitivity) Time (samples) Time (samples) Time (samples) Time (samples)
70 SSM Enhancement Example: Zager & Evans In The Year 2525 I V1 V2 V3 V4 V5 V6 V7 B V8 O
71 SSM Enhancement Example: Zager & Evans In The Year 2525
72 SSM Enhancement Example: Zager & Evans In The Year 2525 Missing relations because of transposed sections
73 SSM Enhancement Example: Zager & Evans In The Year 2525 Idea: Cyclic shift of one of the chroma sequences One semitone up
74 SSM Enhancement Example: Zager & Evans In The Year 2525 Idea: Cyclic shift of one of the chroma sequences Two semitones up
75 SSM Enhancement Example: Zager & Evans In The Year 2525 Idea: Overlay & Maximize Transposition-invariant SSM
76 SSM Enhancement Example: Zager & Evans In The Year 2525 Note: Order of enhancement steps important! Maximization Smoothing & Maximization
77 Similarity Matrix Toolbox Meinard Müller, Nanzhu Jiang, Harald Grohganz SM Toolbox: MATLAB Implementations for Computing and Enhancing Similarity Matrices
78 Overview Introduction Feature Representations Self-Similarity Matrices Audio Thumbnailing Thanks: Jiang, Grosche Peeters Cooper, Foote Goto Levy, Sandler Mauch Sapp Novelty-based Segmentation
79 Audio Thumbnailing General goal: Determine the most representative section ( Thumbnail ) of a given music recording. Example: Zager & Evans In The Year 2525 I V1 V2 V3 V4 V5 V6 V7 B V8 O Example: Brahms Hungarian Dance No. 5 (Ormandy) A1 A2 B1 B2 C A3 B3 B4 Thumbnail is often assumed to be the most repetitive segment
80 Audio Thumbnailing Two steps 1. Path extraction Both steps are problematic! Paths of poor quality (fragmented, gaps) Block-like structures Curved paths 2. Grouping Noisy relations (missing, distorted, overlapping) Transitivity computation difficult Main idea: Do both, path extraction and grouping, jointly One optimization scheme for both steps Stabilizing effect Efficient
81 Audio Thumbnailing Main idea: Do both path extraction and grouping jointly For each audio segment we define a fitness value This fitness value expresses how well the segment explains the entire audio recording The segment with the highest fitness value is considered to be the thumbnail As main technical concept we introduce the notion of a path family
82 Fitness Measure Enhanced SSM
83 Fitness Measure Path over segment Consider a fixed segment
84 Fitness Measure Path over segment Consider a fixed segment Path over segment Induced segment Score is high
85 Fitness Measure Path over segment Consider a fixed segment Path over segment Induced segment Score is high A second path over segment Induced segment Score is not so high
86 Fitness Measure Path over segment Consider a fixed segment Path over segment Induced segment Score is high A second path over segment Induced segment Score is not so high A third path over segment Induced segment Score is very low
87 Fitness Measure Path family Consider a fixed segment A path family over a segment is a family of paths such that the induced segments do not overlap.
88 Fitness Measure Path family Consider a fixed segment A path family over a segment is a family of paths such that the induced segments do not overlap. This is not a path family!
89 Fitness Measure Path family Consider a fixed segment A path family over a segment is a family of paths such that the induced segments do not overlap. This is a path family! (Even though not a good one)
90 Fitness Measure Optimal path family Consider a fixed segment
91 Fitness Measure Optimal path family Consider a fixed segment Consider over the segment the optimal path family, i.e., the path family having maximal overall score. Call this value: Score(segment) Note: This optimal path family can be computed using dynamic programming.
92 Fitness Measure Optimal path family Consider a fixed segment Consider over the segment the optimal path family, i.e., the path family having maximal overall score. Call this value: Score(segment) Furthermore consider the amount covered by the induced segments. Call this value: Coverage(segment)
93 Fitness Measure Fitness Consider a fixed segment P := R := Score(segment) Coverage(segment)
94 Fitness Measure Fitness Consider a fixed segment Self-explanation are trivial! P := R := Score(segment) Coverage(segment)
95 Fitness Measure Fitness Consider a fixed segment Self-explanation are trivial! Subtract length of segment P := R := Score(segment) Coverage(segment) - length(segment) - length(segment)
96 Fitness Measure Fitness Consider a fixed segment Self-explanation are trivial! Subtract length of segment Normalization P := Normalize( Score(segment) - length(segment) ) R := Normalize( Coverage(segment) - length(segment) ) [0,1] [0,1]
97 Fitness Measure Fitness Consider a fixed segment Fitness(segment) F := 2 P R / (P + R) P := Normalize( Score(segment) - length(segment) ) R := Normalize( Coverage(segment) - length(segment) ) [0,1] [0,1]
98 Thumbnail Fitness Scape Plot Fitness Segment length Segment length Segment center Segment center
99 Thumbnail Fitness Scape Plot Fitness Segment length Fitness(segment) Segment length Segment center Segment center
100 Thumbnail Fitness Scape Plot Fitness Segment length Segment center
101 Thumbnail Fitness Scape Plot Fitness Segment length Segment center Note: Self-explanations are ignored fitness is zero
102 Thumbnail Fitness Scape Plot Fitness Segment length Segment center Thumbnail := segment having the highest fitness
103 Thumbnail Fitness Scape Plot Fitness A1 A2 B1 B2 C A3 B3 B4 Example: Brahms Hungarian Dance No. 5 (Ormandy)
104 Thumbnail Fitness Scape Plot Fitness A1 A2 B1 B2 C A3 B3 B4 Example: Brahms Hungarian Dance No. 5 (Ormandy)
105 Thumbnail Fitness Scape Plot Fitness A1 A2 B1 B2 C A3 B3 B4 Example: Brahms Hungarian Dance No. 5 (Ormandy)
106 Thumbnail Fitness Scape Plot Fitness A1 A2 B1 B2 C A3 B3 B4 Example: Brahms Hungarian Dance No. 5 (Ormandy)
107 Scape Plot Example: Brahms Hungarian Dance No. 5 (Ormandy)
108 Scape Plot Coloring according to clustering result (grouping) Example: Brahms Hungarian Dance No. 5 (Ormandy)
109 Scape Plot Coloring according to clustering result (grouping) A1 A2 B1 B2 C A3 B3 B4 Example: Brahms Hungarian Dance No. 5 (Ormandy)
110 Thumbnail Fitness Scape Plot Fitness I V1 V2 V3 V4 V5 V6 V7 B V8 O Example: Zager & Evans In The Year 2525
111 Thumbnail Fitness Scape Plot Fitness I V1 V2 V3 V4 V5 V6 V7 B V8 O Example: Zager & Evans In The Year 2525
112 Overview Introduction Feature Representations Self-Similarity Matrices Thanks: Foote Serra, Grosche, Arcos Goto Tzanetakis, Cook Audio Thumbnailing Novelty-based Segmentation
113 Novelty-based Segmentation General goals: Find instances where musical changes occur. Find transition between subsequent musical parts. Idea (Foote): Use checkerboard-like kernel function to detect corner points on main diagonal of SSM.
114 Novelty-based Segmentation Idea (Foote): Use checkerboard-like kernel function to detect corner points on main diagonal of SSM.
115 Novelty-based Segmentation Idea (Foote): Use checkerboard-like kernel function to detect corner points on main diagonal of SSM.
116 Novelty-based Segmentation Idea (Foote): Use checkerboard-like kernel function to detect corner points on main diagonal of SSM.
117 Novelty-based Segmentation Idea (Foote): Use checkerboard-like kernel function to detect corner points on main diagonal of SSM.
118 Novelty-based Segmentation Idea (Foote): Use checkerboard-like kernel function to detect corner points on main diagonal of SSM. Novelty function using
119 Novelty-based Segmentation Idea (Foote): Use checkerboard-like kernel function to detect corner points on main diagonal of SSM. Novelty function using Novelty function using
120 Novelty-based Segmentation Idea: Find instances where structural changes occur. Structure features Combine global and local aspects within a unifying framework
121 Novelty-based Segmentation Structure features Enhanced SSM
122 Novelty-based Segmentation Structure features Enhanced SSM Time-lag SSM
123 Novelty-based Segmentation Structure features Enhanced SSM Time-lag SSM Cyclic time-lag SSM
124 Novelty-based Segmentation Structure features Enhanced SSM Time-lag SSM Cyclic time-lag SSM Columns as features
125 Novelty-based Segmentation Example: Chopin Mazurka Op. 24, No. 1 SSM Time-lag SSM
126 Novelty-based Segmentation Example: Chopin Mazurka Op. 24, No. 1 SSM Time-lag SSM
127 Novelty-based Segmentation Example: Chopin Mazurka Op. 24, No. 1 SSM Time-lag SSM
128 Novelty-based Segmentation Example: Chopin Mazurka Op. 24, No. 1 SSM Time-lag SSM Structure-based novelty function
129 Conclusions Structure Analysis
130 Conclusions Score Audio MIDI Representations Structure Analysis
131 Conclusions Score Audio MIDI Representations Harmony Musical Aspects Timbre Structure Analysis Tempo
132 Conclusions Score Audio MIDI Representations Musical Aspects Structure Analysis Segmentation Principles Harmony Timbre Tempo Repetition Homogeneity Novelty
133 Conclusions Temporal and Hierarchical Context Score Audio MIDI Representations Musical Aspects Structure Analysis Segmentation Principles Harmony Timbre Tempo Repetition Homogeneity Novelty
134 Conclusions Combined Approaches Hierarchical Approaches Evaluation Explaining Structure MIREX SALAMI-Project Smith, Chew
135 Links SM Toolbox (MATLAB) MSAF: Music Structure Analysis Framework (Python) SALAMI Annotation Data LibROSA (Python) Evaluation: mir_eval (Python) Deep Learning: Boundary Detection Jan Schlüter (PhD thesis)
Audio Structure Analysis
Tutorial T3 A Basic Introduction to Audio-Related Music Information Retrieval Audio Structure Analysis Meinard Müller, Christof Weiß International Audio Laboratories Erlangen meinard.mueller@audiolabs-erlangen.de,
More informationMusic Structure Analysis
Overview Tutorial Music Structure Analysis Part I: Principles & Techniques (Meinard Müller) Coffee Break Meinard Müller International Audio Laboratories Erlangen Universität Erlangen-Nürnberg meinard.mueller@audiolabs-erlangen.de
More informationAudio Structure Analysis
Lecture Music Processing Audio Structure Analysis Meinard Müller International Audio Laboratories Erlangen meinard.mueller@audiolabs-erlangen.de Music Structure Analysis Music segmentation pitch content
More informationMusic Structure Analysis
Tutorial Automatisierte Methoden der Musikverarbeitung 47. Jahrestagung der Gesellschaft für Informatik Music Structure Analysis Meinard Müller, Christof Weiss, Stefan Balke International Audio Laboratories
More informationAudio Structure Analysis
Advanced Course Computer Science Music Processing Summer Term 2009 Meinard Müller Saarland University and MPI Informatik meinard@mpi-inf.mpg.de Music Structure Analysis Music segmentation pitch content
More informationAUTOMATED METHODS FOR ANALYZING MUSIC RECORDINGS IN SONATA FORM
AUTOMATED METHODS FOR ANALYZING MUSIC RECORDINGS IN SONATA FORM Nanzhu Jiang International Audio Laboratories Erlangen nanzhu.jiang@audiolabs-erlangen.de Meinard Müller International Audio Laboratories
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 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 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 informationCS 591 S1 Computational Audio
4/29/7 CS 59 S Computational Audio Wayne Snyder Computer Science Department Boston University Today: Comparing Musical Signals: Cross- and Autocorrelations of Spectral Data for Structure Analysis Segmentation
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 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 informationInformed Feature Representations for Music and Motion
Meinard Müller Informed Feature Representations for Music and Motion Meinard Müller 27 Habilitation, Bonn 27 MPI Informatik, Saarbrücken Senior Researcher Music Processing & Motion Processing Lorentz Workshop
More informationMusic Processing Audio Retrieval Meinard Müller
Lecture Music Processing Audio Retrieval Meinard Müller International Audio Laboratories Erlangen meinard.mueller@audiolabs-erlangen.de Book: Fundamentals of Music Processing Meinard Müller Fundamentals
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 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 informationAUDIO-BASED MUSIC STRUCTURE ANALYSIS
11th International Society for Music Information Retrieval Conference (ISMIR 21) AUDIO-ASED MUSIC STRUCTURE ANALYSIS Jouni Paulus Fraunhofer Institute for Integrated Circuits IIS Erlangen, Germany jouni.paulus@iis.fraunhofer.de
More informationAUDIO-BASED MUSIC STRUCTURE ANALYSIS
AUDIO-ASED MUSIC STRUCTURE ANALYSIS Jouni Paulus Fraunhofer Institute for Integrated Circuits IIS Erlangen, Germany jouni.paulus@iis.fraunhofer.de Meinard Müller Saarland University and MPI Informatik
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 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 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 informationMethods for the automatic structural analysis of music. Jordan B. L. Smith CIRMMT Workshop on Structural Analysis of Music 26 March 2010
1 Methods for the automatic structural analysis of music Jordan B. L. Smith CIRMMT Workshop on Structural Analysis of Music 26 March 2010 2 The problem Going from sound to structure 2 The problem Going
More informationGrouping Recorded Music by Structural Similarity Juan Pablo Bello New York University ISMIR 09, Kobe October 2009 marl music and audio research lab
Grouping Recorded Music by Structural Similarity Juan Pablo Bello New York University ISMIR 09, Kobe October 2009 Sequence-based analysis Structure discovery Cooper, M. & Foote, J. (2002), Automatic Music
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 informationA repetition-based framework for lyric alignment in popular songs
A repetition-based framework for lyric alignment in popular songs ABSTRACT LUONG Minh Thang and KAN Min Yen Department of Computer Science, School of Computing, National University of Singapore We examine
More informationMusic Radar: A Web-based Query by Humming System
Music Radar: A Web-based Query by Humming System Lianjie Cao, Peng Hao, Chunmeng Zhou Computer Science Department, Purdue University, 305 N. University Street West Lafayette, IN 47907-2107 {cao62, pengh,
More informationTOWARD AN INTELLIGENT EDITOR FOR JAZZ MUSIC
TOWARD AN INTELLIGENT EDITOR FOR JAZZ MUSIC G.TZANETAKIS, N.HU, AND R.B. DANNENBERG Computer Science Department, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA 15213, USA E-mail: gtzan@cs.cmu.edu
More informationMusic Segmentation Using Markov Chain Methods
Music Segmentation Using Markov Chain Methods Paul Finkelstein March 8, 2011 Abstract This paper will present just how far the use of Markov Chains has spread in the 21 st century. We will explain some
More informationTowards Supervised Music Structure Annotation: A Case-based Fusion Approach.
Towards Supervised Music Structure Annotation: A Case-based Fusion Approach. Giacomo Herrero MSc Thesis, Universitat Pompeu Fabra Supervisor: Joan Serrà, IIIA-CSIC September, 2014 Abstract Analyzing the
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 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 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 informationEffects of acoustic degradations on cover song recognition
Signal Processing in Acoustics: Paper 68 Effects of acoustic degradations on cover song recognition Julien Osmalskyj (a), Jean-Jacques Embrechts (b) (a) University of Liège, Belgium, josmalsky@ulg.ac.be
More informationA FORMALIZATION OF RELATIVE LOCAL TEMPO VARIATIONS IN COLLECTIONS OF PERFORMANCES
A FORMALIZATION OF RELATIVE LOCAL TEMPO VARIATIONS IN COLLECTIONS OF PERFORMANCES Jeroen Peperkamp Klaus Hildebrandt Cynthia C. S. Liem Delft University of Technology, Delft, The Netherlands jbpeperkamp@gmail.com
More informationA MID-LEVEL REPRESENTATION FOR CAPTURING DOMINANT TEMPO AND PULSE INFORMATION IN MUSIC RECORDINGS
th International Society for Music Information Retrieval Conference (ISMIR 9) A MID-LEVEL REPRESENTATION FOR CAPTURING DOMINANT TEMPO AND PULSE INFORMATION IN MUSIC RECORDINGS Peter Grosche and Meinard
More informationFREISCHÜTZ DIGITAL: A CASE STUDY FOR REFERENCE-BASED AUDIO SEGMENTATION OF OPERAS
FREISCHÜTZ DIGITAL: A CASE STUDY FOR REFERENCE-BASED AUDIO SEGMENTATION OF OPERAS Thomas Prätzlich International Audio Laboratories Erlangen thomas.praetzlich@audiolabs-erlangen.de Meinard Müller International
More informationAUTOMATIC ACCOMPANIMENT OF VOCAL MELODIES IN THE CONTEXT OF POPULAR MUSIC
AUTOMATIC ACCOMPANIMENT OF VOCAL MELODIES IN THE CONTEXT OF POPULAR MUSIC A Thesis Presented to The Academic Faculty by Xiang Cao In Partial Fulfillment of the Requirements for the Degree Master of Science
More informationNew Developments in Music Information Retrieval
New Developments in Music Information Retrieval Meinard Müller 1 1 Saarland University and MPI Informatik, Campus E1.4, 66123 Saarbrücken, Germany Correspondence should be addressed to Meinard Müller (meinard@mpi-inf.mpg.de)
More informationMusic Alignment and Applications. Introduction
Music Alignment and Applications Roger B. Dannenberg Schools of Computer Science, Art, and Music Introduction Music information comes in many forms Digital Audio Multi-track Audio Music Notation MIDI Structured
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 informationAUTOMATIC MAPPING OF SCANNED SHEET MUSIC TO AUDIO RECORDINGS
AUTOMATIC MAPPING OF SCANNED SHEET MUSIC TO AUDIO RECORDINGS Christian Fremerey, Meinard Müller,Frank Kurth, Michael Clausen Computer Science III University of Bonn Bonn, Germany Max-Planck-Institut (MPI)
More informationPopular Song Summarization Using Chorus Section Detection from Audio Signal
Popular Song Summarization Using Chorus Section Detection from Audio Signal Sheng GAO 1 and Haizhou LI 2 Institute for Infocomm Research, A*STAR, Singapore 1 gaosheng@i2r.a-star.edu.sg 2 hli@i2r.a-star.edu.sg
More informationAudio Feature Extraction for Corpus Analysis
Audio Feature Extraction for Corpus Analysis Anja Volk Sound and Music Technology 5 Dec 2017 1 Corpus analysis What is corpus analysis study a large corpus of music for gaining insights on general trends
More informationTopic 11. Score-Informed Source Separation. (chroma slides adapted from Meinard Mueller)
Topic 11 Score-Informed Source Separation (chroma slides adapted from Meinard Mueller) Why Score-informed Source Separation? Audio source separation is useful Music transcription, remixing, search Non-satisfying
More informationARECENT emerging area of activity within the music information
1726 IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 22, NO. 12, DECEMBER 2014 AutoMashUpper: Automatic Creation of Multi-Song Music Mashups Matthew E. P. Davies, Philippe Hamel,
More informationAUTOMASHUPPER: AN AUTOMATIC MULTI-SONG MASHUP SYSTEM
AUTOMASHUPPER: AN AUTOMATIC MULTI-SONG MASHUP SYSTEM Matthew E. P. Davies, Philippe Hamel, Kazuyoshi Yoshii and Masataka Goto National Institute of Advanced Industrial Science and Technology (AIST), Japan
More informationA wavelet-based approach to the discovery of themes and sections in monophonic melodies Velarde, Gissel; Meredith, David
Aalborg Universitet A wavelet-based approach to the discovery of themes and sections in monophonic melodies Velarde, Gissel; Meredith, David Publication date: 2014 Document Version Accepted author manuscript,
More informationMUSIC is a ubiquitous and vital part of the lives of billions
1088 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 5, NO. 6, OCTOBER 2011 Signal Processing for Music Analysis Meinard Müller, Member, IEEE, Daniel P. W. Ellis, Senior Member, IEEE, Anssi
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 informationSHEET MUSIC-AUDIO IDENTIFICATION
SHEET MUSIC-AUDIO IDENTIFICATION Christian Fremerey, Michael Clausen, Sebastian Ewert Bonn University, Computer Science III Bonn, Germany {fremerey,clausen,ewerts}@cs.uni-bonn.de Meinard Müller Saarland
More information10 Visualization of Tonal Content in the Symbolic and Audio Domains
10 Visualization of Tonal Content in the Symbolic and Audio Domains Petri Toiviainen Department of Music PO Box 35 (M) 40014 University of Jyväskylä Finland ptoiviai@campus.jyu.fi Abstract Various computational
More informationAspects of Music. Chord Recognition. Musical Chords. Harmony: The Basis of Music. Musical Chords. Musical Chords. Piece of music. Rhythm.
Aspects of Music Lecture Music Processing Piece of music hord Recognition Meinard Müller International Audio Laboratories rlangen meinard.mueller@audiolabs-erlangen.de Melody Rhythm Harmony Harmony: The
More informationThe song remains the same: identifying versions of the same piece using tonal descriptors
The song remains the same: identifying versions of the same piece using tonal descriptors Emilia Gómez Music Technology Group, Universitat Pompeu Fabra Ocata, 83, Barcelona emilia.gomez@iua.upf.edu Abstract
More informationThe Effect of DJs Social Network on Music Popularity
The Effect of DJs Social Network on Music Popularity Hyeongseok Wi Kyung hoon Hyun Jongpil Lee Wonjae Lee Korea Advanced Institute Korea Advanced Institute Korea Advanced Institute Korea Advanced Institute
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 informationShades of Music. Projektarbeit
Shades of Music Projektarbeit Tim Langer LFE Medieninformatik 28.07.2008 Betreuer: Dominikus Baur Verantwortlicher Hochschullehrer: Prof. Dr. Andreas Butz LMU Department of Media Informatics Projektarbeit
More informationMATCHING MUSICAL THEMES BASED ON NOISY OCR AND OMR INPUT. Stefan Balke, Sanu Pulimootil Achankunju, Meinard Müller
MATCHING MUSICAL THEMES BASED ON NOISY OCR AND OMR INPUT Stefan Balke, Sanu Pulimootil Achankunju, Meinard Müller International Audio Laboratories Erlangen, Friedrich-Alexander-Universität (FAU), Germany
More informationTOWARDS AUTOMATED EXTRACTION OF TEMPO PARAMETERS FROM EXPRESSIVE MUSIC RECORDINGS
th International Society for Music Information Retrieval Conference (ISMIR 9) TOWARDS AUTOMATED EXTRACTION OF TEMPO PARAMETERS FROM EXPRESSIVE MUSIC RECORDINGS Meinard Müller, Verena Konz, Andi Scharfstein
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 informationMODELS of music begin with a representation of the
602 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 18, NO. 3, MARCH 2010 Modeling Music as a Dynamic Texture Luke Barrington, Student Member, IEEE, Antoni B. Chan, Member, IEEE, and
More informationMUSIC SHAPELETS FOR FAST COVER SONG RECOGNITION
MUSIC SHAPELETS FOR FAST COVER SONG RECOGNITION Diego F. Silva Vinícius M. A. Souza Gustavo E. A. P. A. Batista Instituto de Ciências Matemáticas e de Computação Universidade de São Paulo {diegofsilva,vsouza,gbatista}@icmc.usp.br
More informationUSING MUSICAL STRUCTURE TO ENHANCE AUTOMATIC CHORD TRANSCRIPTION
10th International Society for Music Information Retrieval Conference (ISMIR 2009) USING MUSICL STRUCTURE TO ENHNCE UTOMTIC CHORD TRNSCRIPTION Matthias Mauch, Katy Noland, Simon Dixon Queen Mary University
More informationHomework 2 Key-finding algorithm
Homework 2 Key-finding algorithm Li Su Research Center for IT Innovation, Academia, Taiwan lisu@citi.sinica.edu.tw (You don t need any solid understanding about the musical key before doing this homework,
More informationarxiv: v1 [cs.ir] 2 Aug 2017
PIECE IDENTIFICATION IN CLASSICAL PIANO MUSIC WITHOUT REFERENCE SCORES Andreas Arzt, Gerhard Widmer Department of Computational Perception, Johannes Kepler University, Linz, Austria Austrian Research Institute
More informationCitation for published version (APA): Jensen, K. K. (2005). A Causal Rhythm Grouping. Lecture Notes in Computer Science, 3310,
Aalborg Universitet A Causal Rhythm Grouping Jensen, Karl Kristoffer Published in: Lecture Notes in Computer Science Publication date: 2005 Document Version Early version, also known as pre-print Link
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 informationCSC475 Music Information Retrieval
CSC475 Music Information Retrieval Monophonic pitch extraction George Tzanetakis University of Victoria 2014 G. Tzanetakis 1 / 32 Table of Contents I 1 Motivation and Terminology 2 Psychacoustics 3 F0
More informationA CHROMA-BASED SALIENCE FUNCTION FOR MELODY AND BASS LINE ESTIMATION FROM MUSIC AUDIO SIGNALS
A CHROMA-BASED SALIENCE FUNCTION FOR MELODY AND BASS LINE ESTIMATION FROM MUSIC AUDIO SIGNALS Justin Salamon Music Technology Group Universitat Pompeu Fabra, Barcelona, Spain justin.salamon@upf.edu Emilia
More informationRobert Alexandru Dobre, Cristian Negrescu
ECAI 2016 - International Conference 8th Edition Electronics, Computers and Artificial Intelligence 30 June -02 July, 2016, Ploiesti, ROMÂNIA Automatic Music Transcription Software Based on Constant Q
More informationTHE importance of music content analysis for musical
IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 15, NO. 1, JANUARY 2007 333 Drum Sound Recognition for Polyphonic Audio Signals by Adaptation and Matching of Spectrogram Templates With
More informationKrzysztof Rychlicki-Kicior, Bartlomiej Stasiak and Mykhaylo Yatsymirskyy Lodz University of Technology
Krzysztof Rychlicki-Kicior, Bartlomiej Stasiak and Mykhaylo Yatsymirskyy Lodz University of Technology 26.01.2015 Multipitch estimation obtains frequencies of sounds from a polyphonic audio signal Number
More informationDISCOVERY OF REPEATED VOCAL PATTERNS IN POLYPHONIC AUDIO: A CASE STUDY ON FLAMENCO MUSIC. Univ. of Piraeus, Greece
DISCOVERY OF REPEATED VOCAL PATTERNS IN POLYPHONIC AUDIO: A CASE STUDY ON FLAMENCO MUSIC Nadine Kroher 1, Aggelos Pikrakis 2, Jesús Moreno 3, José-Miguel Díaz-Báñez 3 1 Music Technology Group Univ. Pompeu
More informationA comparison and evaluation of approaches to the automatic formal analysis of musical audio
A comparison and evaluation of approaches to the automatic formal analysis of musical audio Jordan B. L. Smith Master of Arts Music Technology Area Department of Music Research Schulich School of Music
More informationRhythm related MIR tasks
Rhythm related MIR tasks Ajay Srinivasamurthy 1, André Holzapfel 1 1 MTG, Universitat Pompeu Fabra, Barcelona, Spain 10 July, 2012 Srinivasamurthy et al. (UPF) MIR tasks 10 July, 2012 1 / 23 1 Rhythm 2
More informationTopic 10. Multi-pitch Analysis
Topic 10 Multi-pitch Analysis What is pitch? Common elements of music are pitch, rhythm, dynamics, and the sonic qualities of timbre and texture. An auditory perceptual attribute in terms of which sounds
More informationChord Recognition. Aspects of Music. Musical Chords. Harmony: The Basis of Music. Musical Chords. Musical Chords. Music Processing.
dvanced ourse omputer Science Music Processing Summer Term 2 Meinard Müller, Verena Konz Saarland University and MPI Informatik meinard@mpi-inf.mpg.de hord Recognition spects of Music Melody Piece of music
More informationAnalysing Musical Pieces Using harmony-analyser.org Tools
Analysing Musical Pieces Using harmony-analyser.org Tools Ladislav Maršík Dept. of Software Engineering, Faculty of Mathematics and Physics Charles University, Malostranské nám. 25, 118 00 Prague 1, Czech
More informationAUDIO MATCHING VIA CHROMA-BASED STATISTICAL FEATURES
AUDIO MATCHING VIA CHROMA-BASED STATISTICAL FEATURES Meinard Müller Frank Kurth Michael Clausen Universität Bonn, Institut für Informatik III Römerstr. 64, D-537 Bonn, Germany {meinard, frank, clausen}@cs.uni-bonn.de
More informationhit), and assume that longer incidental sounds (forest noise, water, wind noise) resemble a Gaussian noise distribution.
CS 229 FINAL PROJECT A SOUNDHOUND FOR THE SOUNDS OF HOUNDS WEAKLY SUPERVISED MODELING OF ANIMAL SOUNDS ROBERT COLCORD, ETHAN GELLER, MATTHEW HORTON Abstract: We propose a hybrid approach to generating
More informationSINGING EXPRESSION TRANSFER FROM ONE VOICE TO ANOTHER FOR A GIVEN SONG. Sangeon Yong, Juhan Nam
SINGING EXPRESSION TRANSFER FROM ONE VOICE TO ANOTHER FOR A GIVEN SONG Sangeon Yong, Juhan Nam Graduate School of Culture Technology, KAIST {koragon2, juhannam}@kaist.ac.kr ABSTRACT We present a vocal
More informationAn Examination of Foote s Self-Similarity Method
WINTER 2001 MUS 220D Units: 4 An Examination of Foote s Self-Similarity Method Unjung Nam The study is based on my dissertation proposal. Its purpose is to improve my understanding of the feature extractors
More informationComputational Modelling of Harmony
Computational Modelling of Harmony Simon Dixon Centre for Digital Music, Queen Mary University of London, Mile End Rd, London E1 4NS, UK simon.dixon@elec.qmul.ac.uk http://www.elec.qmul.ac.uk/people/simond
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 informationMusic Database Retrieval Based on Spectral Similarity
Music Database Retrieval Based on Spectral Similarity Cheng Yang Department of Computer Science Stanford University yangc@cs.stanford.edu Abstract We present an efficient algorithm to retrieve similar
More informationREpeating Pattern Extraction Technique (REPET): A Simple Method for Music/Voice Separation
IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 21, NO. 1, JANUARY 2013 73 REpeating Pattern Extraction Technique (REPET): A Simple Method for Music/Voice Separation Zafar Rafii, Student
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 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 informationCSC475 Music Information Retrieval
CSC475 Music Information Retrieval Symbolic Music Representations George Tzanetakis University of Victoria 2014 G. Tzanetakis 1 / 30 Table of Contents I 1 Western Common Music Notation 2 Digital Formats
More informationPOST-PROCESSING FIDDLE : A REAL-TIME MULTI-PITCH TRACKING TECHNIQUE USING HARMONIC PARTIAL SUBTRACTION FOR USE WITHIN LIVE PERFORMANCE SYSTEMS
POST-PROCESSING FIDDLE : A REAL-TIME MULTI-PITCH TRACKING TECHNIQUE USING HARMONIC PARTIAL SUBTRACTION FOR USE WITHIN LIVE PERFORMANCE SYSTEMS Andrew N. Robertson, Mark D. Plumbley Centre for Digital Music
More informationResearch Article Multiple Scale Music Segmentation Using Rhythm, Timbre, and Harmony
Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 007, Article ID 7305, pages doi:0.55/007/7305 Research Article Multiple Scale Music Segmentation Using Rhythm, Timbre,
More informationInteracting with a Virtual Conductor
Interacting with a Virtual Conductor Pieter Bos, Dennis Reidsma, Zsófia Ruttkay, Anton Nijholt HMI, Dept. of CS, University of Twente, PO Box 217, 7500AE Enschede, The Netherlands anijholt@ewi.utwente.nl
More informationChroma Binary Similarity and Local Alignment Applied to Cover Song Identification
1138 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 16, NO. 6, AUGUST 2008 Chroma Binary Similarity and Local Alignment Applied to Cover Song Identification Joan Serrà, Emilia Gómez,
More informationRepeating Pattern Discovery and Structure Analysis from Acoustic Music Data
Repeating Pattern Discovery and Structure Analysis from Acoustic Music Data Lie Lu, Muyuan Wang 2, Hong-Jiang Zhang Microsoft Research Asia Beijing, P.R. China, 8 {llu, hjzhang}@microsoft.com 2 Department
More information/$ IEEE
IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 17, NO. 6, AUGUST 2009 1159 Music Structure Analysis Using a Probabilistic Fitness Measure and a Greedy Search Algorithm Jouni Paulus,
More informationAutomatic Identification of Samples in Hip Hop Music
Automatic Identification of Samples in Hip Hop Music Jan Van Balen 1, Martín Haro 2, and Joan Serrà 3 1 Dept of Information and Computing Sciences, Utrecht University, the Netherlands 2 Music Technology
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 informationAudio Cover Song Identification using Convolutional Neural Network
Audio Cover Song Identification using Convolutional Neural Network Sungkyun Chang 1,4, Juheon Lee 2,4, Sang Keun Choe 3,4 and Kyogu Lee 1,4 Music and Audio Research Group 1, College of Liberal Studies
More informationMachine Learning Term Project Write-up Creating Models of Performers of Chopin Mazurkas
Machine Learning Term Project Write-up Creating Models of Performers of Chopin Mazurkas Marcello Herreshoff In collaboration with Craig Sapp (craig@ccrma.stanford.edu) 1 Motivation We want to generative
More informationSTRUCTURAL CHANGE ON MULTIPLE TIME SCALES AS A CORRELATE OF MUSICAL COMPLEXITY
STRUCTURAL CHANGE ON MULTIPLE TIME SCALES AS A CORRELATE OF MUSICAL COMPLEXITY Matthias Mauch Mark Levy Last.fm, Karen House, 1 11 Bache s Street, London, N1 6DL. United Kingdom. matthias@last.fm mark@last.fm
More informationWeek 14 Query-by-Humming and Music Fingerprinting. Roger B. Dannenberg Professor of Computer Science, Art and Music Carnegie Mellon University
Week 14 Query-by-Humming and Music Fingerprinting Roger B. Dannenberg Professor of Computer Science, Art and Music Overview n Melody-Based Retrieval n Audio-Score Alignment n Music Fingerprinting 2 Metadata-based
More information