2007 Multimedia System Final Paper Presentation Music Recognition 492410021 蘇冠年 492410070...
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Transcript of 2007 Multimedia System Final Paper Presentation Music Recognition 492410021 蘇冠年 492410070...
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2007 Multimedia System Final Paper Presentation
Music Recognition 492410021 蘇冠年
492410070 蔡尚穎
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Introduction
• In future, the problem is not anymore how to get access to multimedia content, the task is how to find what I’m looking for…
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Music Recognition System
Training
Data Base
Recognition
Result
Input Data
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Before the Algorithm
• Practical Problems
- Disturbance of noise
- Disturbance of Harmonic
- Singer and instrument
- …
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Algorithm I
• Pitch detection - notes, chords …
• Based on frequency domain
- according to music characteristics, it analyzed spectrum at the music pitches
- using Wavelet Transform and DTFT (Discrete-Time Fourier Transform)
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Frequency Analysis
• Music signal is of typical time-frequency distribution
and has short-time steady property
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Frequency Analysis
• Wavelet Transform
- Daub4 Wavelet base by Mallet Algorithm
• DTFT to calculate amplitude
- pitch frequency as parameter ω
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Frequency Analysis
• Analyzed result
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Notes Recognition
• Step 1: Note Voting - 1. analyzed each data by wavelet transform in frequency domain.
- 2. picked out a numbers of notes that have biggest amplitudes in a data as candidate notes.
- 3. count of the appearance times of the candidate notes in several neighbor dada
• Step 2 : denote the different segments of the music
• Step 3 : selected the note that appears most and has the biggest average amplitude
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- A piece of music
- Wave form of the data
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- the spectrogram of segment 1
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- determine the note
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Chords Recognition
• What is the chord ?
• The chord components always have the similar amplitude in frequency domain
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Chords Recognition
• Step 1 : define as a set of candidate notes
and as the amplitude of the notes p
• Step 2 : calculate likelihood coefficient of each note
• Step 3 : coefficient L is the average likelihood coefficient among the notes in a candidate chord
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- A piece of music
- Wave form of the data
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- the spectrogram of segment 1
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- determine the chords
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Algorithm II
• Items of recognizing
• Single-pitched melody
• Multiple-instrument melody
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Pre-Processing
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Adaptive Template-matching
• Phase Tracking
• Template Filtering
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Phase Tracking
z : input signal
r , i : possible sound
p : narrow-band filter
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Phase Tracking
• fs : sampling frequency
• fc : center frequency of the band-pass filter
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Template Filtering
• minimization of J
z(k) : input sum of template waveforms
hn(m) : convolution of the filter coefficients
rn(k) : phase-adjusted waveform
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•
Template Filtering
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Music Stream Networks
• Problem of local information
• Bayesian probabilistic network
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Conclusion
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Reference
1. Zheng Cao, Shengxiao Guan, Zengfu Wang. “A Real-time Algorithm for Music Recognition Based on Wavelet Transform” IEEE June 21 - 23, 2006, Dalian, China
2. Kunio Kashino ,Hiroshi Murase . “Music Recognition using Note Transition Context”
IEEE 1998, NTT Basic Research Laboratories
3. Karlheinz Brandenburg. “Digital Entertainment: Media technologies for the future”
IEEE 2006 , Fraunhofer IDMT & Technische Universität Ilmenau
4. Chen Genfand, Xia Shunren. “The study and prototype system of printed music recognition”. IEEE 2003
5. D Bainbridge , T C Bell. “Dealing with superimposed objects in optical music recognition” IEEE 15-17 July 1997 Universities of Waikato and Canterbury, New Zealand
6. MALLAT'S FAST WAVELET ALGORITHM: RECURSIVE COMPUTATION OF
CONTINUOUS-TIME WAVELET COEFFICIENTS