Real-Time Automatic Music Transcription

1. Real-Time Automatic Music Transcription Goutham Gandhi Nadendla December 3rd , 2009

2. Music Transcription : Music Transcription refers to the analysis of an acoustic music signal so as to write down the pitch, onset time, duration and source of each sound that occurs in it.

3. Pitch : Pitch is a perceptual attribute which allows the ordering of sounds on a frequency related scale extending from low to high. Fundamental frequency(F0) is the corresponding physical term. Onset : Onset refers to the beginning of a musical note or other sound, in which the amplitude rises from zero to an initial peak. It is related to (but different from) the concept of a transient: all musical notes have an onset, but do not necessarily include an initial transient. * Figure From - “A Tutorial on Onset Detection in Music Signals” Juan Pablo Bello, Laurent Daudet, SamerAbdallah, Chris Duxbury, Mike Davies, and Mark B. Sandler, Senior Member, IEEE

4. Timbre : Timber is the quality of musical note or sound or tone that distinguishes different types of sound production, such as voices ormusical instruments. The physical characteristics of sound that mediate the perception of timbre include spectrum and envelope. Timbre is also known in psychoacoustics  as tone quality or tone color. Duration: The perceived duration of a sound has more or less one-to-one mapping to its physical duration in cases where this can be unambiguously determined.

5. Present Project : • Onset Detection • Duration • Pitch Detection Real Time Processing (Done with the help of Java Sound Objects).

6. Onset Detection: True positives : Correct onset detections relative to the total number of existing onsets. False positives : Erroneous detections relative to the number of detected onsets. * Figure & Results From - “A Tutorial on Onset Detection in Music Signals” Juan Pablo Bello, Laurent Daudet, SamerAbdallah, Chris Duxbury, Mike Davies, and Mark B. Sandler, Senior Member, IEEE

7. Perceptual onset detector by AnssiKlapuri. In this implementation, a filter bank divides the signal into eight non overlappingbands. In each band, onset times and intensities are detected and finally combined. The filter-bank model is used as an approximation to the mechanics of the human cochlea. * Figure from “SOUND ONSET DETECTION BY APPLYING PSYCHOACOUSTIC KNOWLEDGE” AnssiKlapuri,Signal Processing Laboratory, Tampere University of Technology

8. Pitch Detection : (F0 Estimation) • Time-Domain Methods: • Autocorrelation • The YIN Estimator • Frequency-Domain Methods: • Component Frequency Ratios • Filter-Based Methods • CepstrumAnalysis • Multi-Resolution Methods • Statistical Frequency Domain Methods: • Neural Networks • Maximum Likelihood Estimators • Other : • Human Auditory Modeling • Frequency Estimator Tracking * Retreived from – “Pitch Extraction and Fundamental Frequency: History and Current Techniques” David Gerhard Technical Report TR-CS 2003-06 November, 2003

9. AnssiKlapuri’s Multiple F0 Estimator : * Figure from paper– “A PERCEPTUALLY MOTIVATED MULTIPLE-F0 ESTIMATION METHOD” AnssiKlapuriInstituteof Signal Processing, Tampere University of Technology

10. Demo & Evaluation: Partially Complete !!!!! Toolboxes Used: MIR Toolbox MIDI Toolbox Signal Processing Toolbox

11. Future Work: • Present  Final Paper • Test & Evaluate for Polyphonic Music • Final Paper  …… • Timbre Analysis

12. Thank You

13. Algorithm: Multiple F0 Estimator of Klapuri