1. Music Signal Processing
電信碩一 陳秉鴻 Ian

3. Detect each note’s onset in melody. Separate each note.
Frequency & Tempo
Frequency -> MIDI number
Tempo analysis
HMM Select
Using HMM to ignore not likely answer.
Recognition
Matching pattern and giving score.

4. Onset detection

5. STFT

6. Phase-base analyze

7. Match Filter

8. Fundamental frequency

9. Harmonic Product Spectrum

10. Frequency -> Midi

11. Recognition

12. Recent work

13. Spectral Flux Based Novelty Curve
STFT -> 𝑋(𝑛,𝑘) 2
𝛾(𝑛,𝑘)=𝑙𝑜𝑔⁡(1+𝑐. 𝑋(𝑛,𝑘) 2 )
𝑆𝐹(𝑛,𝑘)= 𝛾 𝑛+1,𝑘 −𝛾(𝑛,𝑘) ≥0
𝑁𝐶(𝑛)= 𝑘=0 𝑁 2 −1 𝑆𝐹(𝑛,𝑘)

14. Spectral Flux Based Novelty Curve
STFT -> 𝑋(𝑛,𝑘)
𝛾(𝑛,𝑘)=𝑙𝑜𝑔⁡(1+𝑐. 𝑋(𝑛,𝑘) )
𝑆𝐹(𝑛,𝑘)= 𝛾 𝑛+1,𝑘 −𝛾(𝑛,𝑘) ≥0
𝑁𝐶(𝑛)= 𝑘=0 𝑁 2 −1 𝑆𝐹(𝑛,𝑘)

15. Threshold
𝑔(𝑛)= 𝑖=𝑛 𝑛+𝑊 𝑁𝐶(𝑖)
𝑡 𝑛 =𝑐+𝜆 𝑔 𝑛 −𝑔(𝑛−ℎ)

16. Result

17. Reference
Energy-Weighted Multi-Band Novelty Functions for Onset Detection in Piano Music
A tutorial on onset detection in music signals
Phase-based note onset detection for music signals
Complex domain onset detection for musical signals
Efficient Pitch Detection Techniques for Interactive Music
YIN, a fundamental frequency estimator for speech and music
Singing voice analysis and editing based on mutually dependent F0 estimation and source separation
A Method Combining LPC-Based Cepstrum and Harmonic Product Spectrum for Pitch Detection
Classification of melodies by composer with hidden Markov models
Real time Pattern Based Melodic Query for Music Continuation System
Query by humming of midi and audio using locality sensitive hashing
A new approach to query by humming in music retrieval