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Music Transcription through Statistical Analysis Group 3 Austin Assavavallop, William Feater, Greg Heim, Philipp Pfieffenberger, Wamba Yves Design Phase.

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Presentation on theme: "Music Transcription through Statistical Analysis Group 3 Austin Assavavallop, William Feater, Greg Heim, Philipp Pfieffenberger, Wamba Yves Design Phase."— Presentation transcript:

1 Music Transcription through Statistical Analysis Group 3 Austin Assavavallop, William Feater, Greg Heim, Philipp Pfieffenberger, Wamba Yves Design Phase Presentation

2 Music Transcription Challenge: Decode note information from a monophonic music signal Overview of chosen approach –Noise vs. Signal –Pitch Detection –Timing Analysis –Note Detection Development and Implementation

3 Sound Waves to Tablature Sample stream into frames Identify events frame-by-frame Control a state machine with frame events State machine reports each note’s onset, velocity, pitch, and pauses.

4 What’s music, anyway? Notes –Time limited –Periodic –Audible frequencies –Random phase offset –Considerable* signal energy Rests –Not periodic –Relatively little* signal energy (noise) *Comparatively

5 Noise Characterization Notes vs. Rests –High signal energy: Note –Low signal energy: Rest How much energy is due to noise? –Assume system noise at start of operation –Calculate Mean, Standard Deviation Assume Gaussian noise –Mean ~ 0 –Expected Noise Amplitude =~ Std. Dev. Set threshold for pause –P(High Signal Energy | only noise) =~ 0 –Threshold ~= 3 * Std. Dev. of noise

6 Pitch Detection Pitch Detector Period N p Sample Frame s(n)

7 Pitch Detection Fundamental Period

8 Pitch Detection: A4

9 Pitch Detection Assume western scale –f A1 =55Hz, f A2 =110Hz,.. f AN =55*2 N –Determine frequency presence in signal Take advantage of period multiples Probe signal for all notes of first octave Probe multiples of closest note Fundamental = lowest harmonic period

10 Pitch Detection: Correlation s(n) : Sampled frame, N p = attempted period Caveat: Like all frequency detection techniques, correlation leads to a tradeoff between the lowest detectable frequency and timing accuracy.

11 Timing Analysis Pitch N p A V Sample Frame s(n) Amplitude Variance

12 Timing Analysis Detect frame’s fundamental frequency Divide frame into one period blocks Measure energy of each block

13 Timing Analysis Variance: Variance of block energies Frame Amplitude: Average block energy

14 Envelope Analysis System Overview Pitch Detector Frame s(n) = [0..N] Pitch Amplitude Variance Controller Start, End time Velocity Pitch N p A V

15 Controller Start, End time Velocity Pitch Pitch N p Amplitude AVariance V

16 Controller Logic InitializationNew NoteStable NoteRest ΔN p ΔA ΔN p || ΔV ΔA && ΔV ΔA ΔN p && ΔV ΔA Only accessible before the first Stable Note Report Event

17 Implementation Design Approach Modularization Time domain – Timing Analysis, Controller Frequency domain – Pitch Detection Testing and Integration Implementation issues Processing within 45.3ms Optimization using libraries GUI for note interpretation

18 Questions?


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