1. 2007 Multimedia System Final Paper Presentation Music Recognition 492410021 蘇冠年 492410070 蔡尚穎

2. 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…

3. Music Recognition System Training Data Base Recognition Result Input Data

4. Before the Algorithm Practical Problems - Disturbance of noise - Disturbance of Harmonic - Singer and instrument - …

5. 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)

6. Frequency Analysis Music signal is of typical time-frequency distribution and has short-time steady property

7. Frequency Analysis Wavelet Transform - Daub4 Wavelet base by Mallet Algorithm DTFT to calculate amplitude - pitch frequency as parameter ω

8. Frequency Analysis Analyzed result

9. 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

10. - A piece of music - Wave form of the data

11. - the spectrogram of segment 1

12. - determine the note

13. Chords Recognition What is the chord ? The chord components always have the similar amplitude in frequency domain

14. 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

15. - A piece of music - Wave form of the data

16. - the spectrogram of segment 1

17. - determine the chords

18. Algorithm II Items of recognizing Single-pitched melody Multiple-instrument melody

20. Pre-Processing

21. Adaptive Template-matching Phase Tracking Template Filtering

22. Phase Tracking z : input signal r, i : possible sound p : narrow-band filter

23. Phase Tracking fs : sampling frequency fc : center frequency of the band-pass filter

24. Template Filtering minimization of J z(k) : input sum of template waveforms hn(m) : convolution of the filter coefficients rn(k) : phase-adjusted waveform

25. Template Filtering

26. Music Stream Networks Problem of local information Bayesian probabilistic network

28. Conclusion

29. Reference 1.Zheng Cao, Shengxiao Guan, Zengfu Wang. “A Real-time Algorithm for Music Recogni tion 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 recognit ion”. IEEE 2003 5.D Bainbridge, T C Bell. “Dealing with superimposed objects in optical music recogniti on” 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