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2007 Multimedia System Final Paper Presentation Music Recognition. 492410021 蘇冠年 492410070 蔡尚穎. 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…. Music Recognition System. Data Base. Input Data. - PowerPoint PPT Presentation
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2007 Multimedia System Final Paper Presentation
Music Recognition 492410021 蘇冠年
492410070 蔡尚穎
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…
Music Recognition System
Training
Data Base
Recognition
Result
Input Data
Before the Algorithm
• Practical Problems
- Disturbance of noise
- Disturbance of Harmonic
- Singer and instrument
- …
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)
Frequency Analysis
• Music signal is of typical time-frequency distribution
and has short-time steady property
Frequency Analysis
• Wavelet Transform
- Daub4 Wavelet base by Mallet Algorithm
• DTFT to calculate amplitude
- pitch frequency as parameter ω
Frequency Analysis
• Analyzed result
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
- A piece of music
- Wave form of the data
- the spectrogram of segment 1
- determine the note
Chords Recognition
• What is the chord ?
• The chord components always have the similar amplitude in frequency domain
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
- A piece of music
- Wave form of the data
- the spectrogram of segment 1
- determine the chords
Algorithm II
• Items of recognizing
• Single-pitched melody
• Multiple-instrument melody
Pre-Processing
Adaptive Template-matching
• Phase Tracking
• Template Filtering
Phase Tracking
z : input signal
r , i : possible sound
p : narrow-band filter
Phase Tracking
• fs : sampling frequency
• fc : center frequency of the band-pass filter
Template Filtering
• minimization of J
z(k) : input sum of template waveforms
hn(m) : convolution of the filter coefficients
rn(k) : phase-adjusted waveform
•
Template Filtering
Music Stream Networks
• Problem of local information
• Bayesian probabilistic network
Conclusion
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