musi-6201 | computational music analysis...inference meta data feature extraction dimensionality...
TRANSCRIPT
![Page 1: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/1.jpg)
overview intro content ACA summary course outline
MUSI-6201 — Computational Music AnalysisPart 2: Introduction
alexander lerch
November 4, 2015
![Page 2: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/2.jpg)
overview intro content ACA summary course outline
introductionoverview
text bookChapter 1: Introduction (pp. 1–6)
sources: slides (latex) & Matlab
github repository
lecture contentwhat is audio content analysis?what are typical applications?what is audio content?what are the processing blocks of a typical ACA system?
![Page 3: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/3.jpg)
overview intro content ACA summary course outline
introductionoverview
text bookChapter 1: Introduction (pp. 1–6)
sources: slides (latex) & Matlab
github repository
lecture contentwhat is audio content analysis?what are typical applications?what is audio content?what are the processing blocks of a typical ACA system?
![Page 4: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/4.jpg)
overview intro content ACA summary course outline
introductionoverview
text bookChapter 1: Introduction (pp. 1–6)
sources: slides (latex) & Matlab
github repository
lecture contentwhat is audio content analysis?what are typical applications?what is audio content?what are the processing blocks of a typical ACA system?
![Page 5: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/5.jpg)
overview intro content ACA summary course outline
introductionoverview
text bookChapter 1: Introduction (pp. 1–6)
sources: slides (latex) & Matlab
github repository
lecture contentwhat is audio content analysis?what are typical applications?what is audio content?what are the processing blocks of a typical ACA system?
![Page 6: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/6.jpg)
overview intro content ACA summary course outline
introductionoverview
text bookChapter 1: Introduction (pp. 1–6)
sources: slides (latex) & Matlab
github repository
lecture contentwhat is audio content analysis?what are typical applications?what is audio content?what are the processing blocks of a typical ACA system?
![Page 7: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/7.jpg)
overview intro content ACA summary course outline
introductionaudio content analysis — terminology
goalextract information about the content of audio data
terminologymusic information retrieval (MIR):
analysis and retrieval of music databoth audio and symbolic data
machine listening & computer audition
focus on the recognition and understanding of music
computational auditory scene analysis (CASA)
focus on human perception & cognition, understanding of theauditory scene
![Page 8: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/8.jpg)
overview intro content ACA summary course outline
introductionaudio content analysis — terminology
goalextract information about the content of audio data
terminologymusic information retrieval (MIR):
analysis and retrieval of music databoth audio and symbolic data
machine listening & computer audition
focus on the recognition and understanding of music
computational auditory scene analysis (CASA)
focus on human perception & cognition, understanding of theauditory scene
![Page 9: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/9.jpg)
overview intro content ACA summary course outline
introductionaudio content analysis — research field
interdisciplinarydigital signal processingmachine learning / data miningmusicologypsycho-acoustics. . .
communityISMIR: ismir.net
annual conferencescumulative list of conference papersISMIR-Community mailing listMIREX: MIR Evaluation eXchange
related publicationsconferences: ICASSP, ICME, SMC, DAFx, ACM MM, . . .journals: TASLP, Computer Music, JNMR, JAES, . . .
![Page 10: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/10.jpg)
overview intro content ACA summary course outline
introductionaudio content analysis — research field
interdisciplinarydigital signal processingmachine learning / data miningmusicologypsycho-acoustics. . .
communityISMIR: ismir.net
annual conferencescumulative list of conference papersISMIR-Community mailing listMIREX: MIR Evaluation eXchange
related publicationsconferences: ICASSP, ICME, SMC, DAFx, ACM MM, . . .journals: TASLP, Computer Music, JNMR, JAES, . . .
![Page 11: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/11.jpg)
overview intro content ACA summary course outline
introductionaudio content analysis — research field
interdisciplinarydigital signal processingmachine learning / data miningmusicologypsycho-acoustics. . .
communityISMIR: ismir.net
annual conferencescumulative list of conference papersISMIR-Community mailing listMIREX: MIR Evaluation eXchange
related publicationsconferences: ICASSP, ICME, SMC, DAFx, ACM MM, . . .journals: TASLP, Computer Music, JNMR, JAES, . . .
![Page 12: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/12.jpg)
overview intro content ACA summary course outline
introductionapplications
organization in large databases
search & retrieval, classification, similarity
interfaces to search and retrieval
fingerprinting, query-by-humming systems
music visualizationsymbolic (bars, harmony, score, . . . ), similarity mappings
adaptive processingadaptive effect parametrization or algorithm selection
adaptive interactionplaylist generation, recommendation
![Page 13: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/13.jpg)
overview intro content ACA summary course outline
introductionapplications
organization in large databases
search & retrieval, classification, similarity
interfaces to search and retrieval
fingerprinting, query-by-humming systems
music visualizationsymbolic (bars, harmony, score, . . . ), similarity mappings
adaptive processingadaptive effect parametrization or algorithm selection
adaptive interactionplaylist generation, recommendation
![Page 14: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/14.jpg)
overview intro content ACA summary course outline
introductionapplications
organization in large databases
search & retrieval, classification, similarity
interfaces to search and retrieval
fingerprinting, query-by-humming systems
music visualizationsymbolic (bars, harmony, score, . . . ), similarity mappings
adaptive processingadaptive effect parametrization or algorithm selection
adaptive interactionplaylist generation, recommendation
![Page 15: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/15.jpg)
overview intro content ACA summary course outline
introductionapplications
organization in large databases
search & retrieval, classification, similarity
interfaces to search and retrieval
fingerprinting, query-by-humming systems
music visualizationsymbolic (bars, harmony, score, . . . ), similarity mappings
adaptive processingadaptive effect parametrization or algorithm selection
adaptive interactionplaylist generation, recommendation
![Page 16: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/16.jpg)
overview intro content ACA summary course outline
introductionapplications
organization in large databases
search & retrieval, classification, similarity
interfaces to search and retrieval
fingerprinting, query-by-humming systems
music visualizationsymbolic (bars, harmony, score, . . . ), similarity mappings
adaptive processingadaptive effect parametrization or algorithm selection
adaptive interactionplaylist generation, recommendation
![Page 17: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/17.jpg)
overview intro content ACA summary course outline
introduction(commercial) examples
recommendation, playlist generation
fingerprinting
score following
(multi-) pitch detection
![Page 18: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/18.jpg)
overview intro content ACA summary course outline
introduction(commercial) examples
recommendation, playlist generation
fingerprinting
score following
(multi-) pitch detection
![Page 19: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/19.jpg)
overview intro content ACA summary course outline
introduction(commercial) examples
recommendation, playlist generation
fingerprinting
score following
(multi-) pitch detection
![Page 20: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/20.jpg)
overview intro content ACA summary course outline
introduction(commercial) examples
recommendation, playlist generation
fingerprinting
score following
(multi-) pitch detection
![Page 21: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/21.jpg)
overview intro content ACA summary course outline
audio contentsources
what are the sources of (musical) audio content?
1 score:definition of musical ideas“blue-print” of the musicexamples: melody, key, harmony, rhythmic patterns, . . .
2 performance:unique acoustic renditioninformation in the score is interpreted, modified, added toexamples: (micro-)tempo, dynamics, intonation, . . .
3 production:aesthetic choicesediting & processingexamples: sound quality (EQ, microphone positioning),changes in timing and pitch
![Page 22: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/22.jpg)
overview intro content ACA summary course outline
audio contentsources
what are the sources of (musical) audio content?
1 score:definition of musical ideas“blue-print” of the musicexamples: melody, key, harmony, rhythmic patterns, . . .
2 performance:unique acoustic renditioninformation in the score is interpreted, modified, added toexamples: (micro-)tempo, dynamics, intonation, . . .
3 production:aesthetic choicesediting & processingexamples: sound quality (EQ, microphone positioning),changes in timing and pitch
![Page 23: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/23.jpg)
overview intro content ACA summary course outline
audio contentsources
what are the sources of (musical) audio content?
1 score:definition of musical ideas“blue-print” of the musicexamples: melody, key, harmony, rhythmic patterns, . . .
2 performance:unique acoustic renditioninformation in the score is interpreted, modified, added toexamples: (micro-)tempo, dynamics, intonation, . . .
3 production:aesthetic choicesediting & processingexamples: sound quality (EQ, microphone positioning),changes in timing and pitch
![Page 24: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/24.jpg)
overview intro content ACA summary course outline
audio contentsources
what are the sources of (musical) audio content?
1 score:definition of musical ideas“blue-print” of the musicexamples: melody, key, harmony, rhythmic patterns, . . .
2 performance:unique acoustic renditioninformation in the score is interpreted, modified, added toexamples: (micro-)tempo, dynamics, intonation, . . .
3 production:aesthetic choicesediting & processingexamples: sound quality (EQ, microphone positioning),changes in timing and pitch
![Page 25: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/25.jpg)
overview intro content ACA summary course outline
audio contenttechnical categories
audio content can be structured into 5 technical fundamentalcategories:
1 timbral: related to sound quality
examples: instrument(ation), playing technique, venue, audioprocessing, . . .
2 intensity-related: related to musical dynamics
examples: accents, loudness, . . .
3 tonal: related to pitch
examples: melody, chords, intonation, vibrato, . . .
4 temporal: related to rhythm and tempo
examples: timing, meter, rhythmic patterns, . . .
5 statistical & technical: related to signal properties
examples: amplitude distribution, number of zero crossings,. . .
![Page 26: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/26.jpg)
overview intro content ACA summary course outline
audio contenttechnical categories
audio content can be structured into 5 technical fundamentalcategories:
1 timbral: related to sound quality
examples: instrument(ation), playing technique, venue, audioprocessing, . . .
2 intensity-related: related to musical dynamics
examples: accents, loudness, . . .
3 tonal: related to pitch
examples: melody, chords, intonation, vibrato, . . .
4 temporal: related to rhythm and tempo
examples: timing, meter, rhythmic patterns, . . .
5 statistical & technical: related to signal properties
examples: amplitude distribution, number of zero crossings,. . .
![Page 27: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/27.jpg)
overview intro content ACA summary course outline
audio contenttechnical categories
audio content can be structured into 5 technical fundamentalcategories:
1 timbral: related to sound quality
examples: instrument(ation), playing technique, venue, audioprocessing, . . .
2 intensity-related: related to musical dynamics
examples: accents, loudness, . . .
3 tonal: related to pitch
examples: melody, chords, intonation, vibrato, . . .
4 temporal: related to rhythm and tempo
examples: timing, meter, rhythmic patterns, . . .
5 statistical & technical: related to signal properties
examples: amplitude distribution, number of zero crossings,. . .
![Page 28: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/28.jpg)
overview intro content ACA summary course outline
audio contenttechnical categories
audio content can be structured into 5 technical fundamentalcategories:
1 timbral: related to sound quality
examples: instrument(ation), playing technique, venue, audioprocessing, . . .
2 intensity-related: related to musical dynamics
examples: accents, loudness, . . .
3 tonal: related to pitch
examples: melody, chords, intonation, vibrato, . . .
4 temporal: related to rhythm and tempo
examples: timing, meter, rhythmic patterns, . . .
5 statistical & technical: related to signal properties
examples: amplitude distribution, number of zero crossings,. . .
![Page 29: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/29.jpg)
overview intro content ACA summary course outline
audio contenttechnical categories
audio content can be structured into 5 technical fundamentalcategories:
1 timbral: related to sound quality
examples: instrument(ation), playing technique, venue, audioprocessing, . . .
2 intensity-related: related to musical dynamics
examples: accents, loudness, . . .
3 tonal: related to pitch
examples: melody, chords, intonation, vibrato, . . .
4 temporal: related to rhythm and tempo
examples: timing, meter, rhythmic patterns, . . .
5 statistical & technical: related to signal properties
examples: amplitude distribution, number of zero crossings,. . .
![Page 30: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/30.jpg)
overview intro content ACA summary course outline
audio contenttechnical categories
audio content can be structured into 5 technical fundamentalcategories:
1 timbral: related to sound quality
examples: instrument(ation), playing technique, venue, audioprocessing, . . .
2 intensity-related: related to musical dynamics
examples: accents, loudness, . . .
3 tonal: related to pitch
examples: melody, chords, intonation, vibrato, . . .
4 temporal: related to rhythm and tempo
examples: timing, meter, rhythmic patterns, . . .
5 statistical & technical: related to signal properties
examples: amplitude distribution, number of zero crossings,. . .
![Page 31: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/31.jpg)
overview intro content ACA summary course outline
audio content analysissystem overview
audiosignal
featureextraction
decision,interpretation,classification,
inference
metadata
feature extractiondimensionality reductionmeaningful representation
classificationmap or convert feature tocomprehensible domain
![Page 32: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/32.jpg)
overview intro content ACA summary course outline
audio content analysissystem overview
audiosignal
featureextraction
decision,interpretation,classification,
inference
metadata
feature extractiondimensionality reductionmeaningful representation
classificationmap or convert feature tocomprehensible domain
![Page 33: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/33.jpg)
overview intro content ACA summary course outline
audio content analysissystem overview
audiosignal
featureextraction
decision,interpretation,classification,
inference
metadata
feature extractiondimensionality reductionmeaningful representation
classificationmap or convert feature tocomprehensible domain
![Page 34: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/34.jpg)
overview intro content ACA summary course outline
summarylecture content
what is audio content?
what are the technical categories of interest?
what are the typical processing blocks of an ACA system?
![Page 35: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/35.jpg)
overview intro content ACA summary course outline
summarylecture content
what is audio content?
what are the technical categories of interest?
what are the typical processing blocks of an ACA system?
![Page 36: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/36.jpg)
overview intro content ACA summary course outline
summarylecture content
what is audio content?
what are the technical categories of interest?
what are the typical processing blocks of an ACA system?
![Page 37: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/37.jpg)
overview intro content ACA summary course outline
course outlineoverview 1/2
1 fundamentalsdigital audio signalsconvolution & block based processingFourier transform and filterscorrelation
2 instantaneous featuresaudio pre-processingstatistical and spectral featuresfeature post-processing
3 intensitylevel & loudness
4 tonal analysisfundamental frequencytuning frequencykey and chords
![Page 38: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/38.jpg)
overview intro content ACA summary course outline
course outlineoverview 1/2
1 fundamentalsdigital audio signalsconvolution & block based processingFourier transform and filterscorrelation
2 instantaneous featuresaudio pre-processingstatistical and spectral featuresfeature post-processing
3 intensitylevel & loudness
4 tonal analysisfundamental frequencytuning frequencykey and chords
![Page 39: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/39.jpg)
overview intro content ACA summary course outline
course outlineoverview 1/2
1 fundamentalsdigital audio signalsconvolution & block based processingFourier transform and filterscorrelation
2 instantaneous featuresaudio pre-processingstatistical and spectral featuresfeature post-processing
3 intensitylevel & loudness
4 tonal analysisfundamental frequencytuning frequencykey and chords
![Page 40: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/40.jpg)
overview intro content ACA summary course outline
course outlineoverview 1/2
1 fundamentalsdigital audio signalsconvolution & block based processingFourier transform and filterscorrelation
2 instantaneous featuresaudio pre-processingstatistical and spectral featuresfeature post-processing
3 intensitylevel & loudness
4 tonal analysisfundamental frequencytuning frequencykey and chords
![Page 41: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/41.jpg)
overview intro content ACA summary course outline
course outlineoverview 2/2
5 temporal analysisonset detectiontempo & beatdownbeat & time signature
6 genre, similarity & mood
7 alignmentaudio-to-audioaudio-to-score
8 audio fingerprinting
9 structural segmentation
![Page 42: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/42.jpg)
overview intro content ACA summary course outline
course outlineoverview 2/2
5 temporal analysisonset detectiontempo & beatdownbeat & time signature
6 genre, similarity & mood
7 alignmentaudio-to-audioaudio-to-score
8 audio fingerprinting
9 structural segmentation
![Page 43: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/43.jpg)
overview intro content ACA summary course outline
course outlineoverview 2/2
5 temporal analysisonset detectiontempo & beatdownbeat & time signature
6 genre, similarity & mood
7 alignmentaudio-to-audioaudio-to-score
8 audio fingerprinting
9 structural segmentation
![Page 44: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/44.jpg)
overview intro content ACA summary course outline
course outlineoverview 2/2
5 temporal analysisonset detectiontempo & beatdownbeat & time signature
6 genre, similarity & mood
7 alignmentaudio-to-audioaudio-to-score
8 audio fingerprinting
9 structural segmentation
![Page 45: MUSI-6201 | Computational Music Analysis...inference meta data feature extraction dimensionality reduction meaningful representation ... convolution & block based processing Fourier](https://reader034.vdocuments.us/reader034/viewer/2022042220/5ec6566b4faae761ee4db741/html5/thumbnails/45.jpg)
overview intro content ACA summary course outline
course outlineoverview 2/2
5 temporal analysisonset detectiontempo & beatdownbeat & time signature
6 genre, similarity & mood
7 alignmentaudio-to-audioaudio-to-score
8 audio fingerprinting
9 structural segmentation