Query by Tapping 敲擊選歌

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Query by Tapping 敲擊選歌. J.-S. Roger Jang ( 張智星 ) Multimedia Information Retrieval Lab CS Dept., Tsing Hua Univ., Taiwan http://mirlab.org/jang. Query by Tapping. Goal: Music search based on uses’ tapping (at notes’ onsets) over the microphone/keyboard Characteristics - PowerPoint PPT Presentation

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112/04/22 1

Query by Tapping敲擊選歌

J.-S. Roger Jang ( 張智星 )Multimedia Information Retrieval Lab

CS Dept., Tsing Hua Univ., Taiwan

http://mirlab.org/jang

-2-

Query by Tapping

Goal: Music search based on uses’ tapping (at notes’

onsets) over the microphone/keyboardCharacteristics

Only note duration is used for comparison, note pitch is discarded.

A hard task for human to recognize (which is different from query by singing/humming)Try this…

-3-

Query by Tapping

Goal: Music search based on uses’ tapping (at notes’

onsets) over the microphone/keyboardCharacteristics

Only note duration is used for comparison, note pitch is discarded.

A hard task for human to recognize (which is different from query by singing/humming)Try this…

-4-

Query by Tapping

Challenges: Users is unlikely to use the same tempo as the

intended song Users tend to lose notes instead of gaining ones We have about 13,000 songs in the database

Major approach: A distance measure based on dynamic

programming

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Feature Extraction via Microphone

Microphone input:

After frame blocking, energy computation, and thresholding:

-7-

Performance Evaluation of Onset Detection

simSequence.m

0 1 2 3 4 5 6

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Computed

GT

precision=3/6=0.5recall=3/5=0.6f-measure=2pr/(p+r)=0.5455

-8-

Similarity Comparison with Songs in Database

A fast method based on IOI ratios Compute the IOI ratios for both query and db IOI

vectors Compute the Euclidean distance these two ratio

vectors

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Music Note Alignment

t(3)

t: test (input) IOI vectorr: reference IOI vector

r(1)t(1)

t(2)r(2)

r(3)

NormalizationAlignment

by DP

t r t r t r

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Normalization

Normalization to have

(Multiplication of 1000 to guarantee high resolution in fixed-point computation.)

)~,~(min),(2~2

qppq

rtDrtdist

))):1((/):1(*1000(~))(/*1000(~

qrsumqrroundr

tsumtroundt

q

1000)(~)(~

11

q

jq

p

i

irit

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Dynamic-programming-based Distance

i

j

t(i-2)

r(j-1)

1~1,0),1(

1~1,0)1,(

)1()1()2()1,2(

)1()1()1,1(

)1()1()2()2,1(

min

),(

2

1

njjD

miiD

jrititjiD

jritjiD

itjrjrjiD

jiD

),( jiD

t: test IOI vector of length mr: reference IOI vector of length n

Recurrent relation:

r(j-2)

t(i-1)t(1) t(2)

r(1)

r(2)

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Experimental Environment

269 test wave files of tapping clips 9 contributors (7 males, 2 females) Wave length: 15 seconds Wave format: PCM, 11025Hz, 8bits, Mono Start position: Beginning of a song

Environment Pentium III 800, 256MB RAM

Database 11,744 MIDI files

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Test Results Using Clips of 15 Seconds

Average response time: 3.42 seconds (29.98 notes)

Recognition rates: Top-1 (top 0.0085%): 15% Top-10 (top 0.085%): 51% Top-100 (top 0.85%): 80%

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Error Analysis

Errors analysis of low-ranked clips Some users cannot tap consistently through 15

seconds Feature extraction is not robust enough to handle

noisy input. Some MIDI files are not faithful rendition of the

original tunes. Users cannot keep up with short consecutive notes.

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Recog. Rates w.r.t. Tapping Duration

Top-100 and 1000 curves level off after 10 seconds.

Top-100 curve does not go up monotonically.

Top-100

Top-10

Top-1000

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Demo

No. of MIDI files: 12982

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Partial List of Songs All I have to do is dream You are my sunshine Beautiful Sunday Do Re Mi Feelings A time for us Love is blue Let it be me My way Love story More than I can say Only you Rain and tears

Rhythm of the rain Rose Rose I love you The sound of silence Unchained melody We are the world Yesterday I just call to say I love you Close to you Mr. Lonely Ben Hey Jude Donna Donna Sealed with a kiss

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Potential Applications

Interactive toysBeat-tracking training and gamesSong retrieval in noisy karaoke bars

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Conclusions

Our MIR system is the first one with query-by-tapping capability.

Rhythm-based search can be used in conjunction with pitch-contour-based search to achieve a better recognition rate.

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Future Work

Search scope expansion How to retrieve MP3 or CD music directly?

Scale-up by hierarchical filtering method How to deal with database with 100,000 songs? What if the user tap from anywhere in the middle

of a song?