Formant estimation in Singing Studiovoicestudies/artts/doc/presentations/... · •Singing analysis...
Transcript of Formant estimation in Singing Studiovoicestudies/artts/doc/presentations/... · •Singing analysis...
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Formant estimation in Singing Studio
PROJECT MEETING
Vítor Almeida
Faculdade de Engenharia da Universidade do Porto, Porto
January 19th, 2013
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Formant estimation algorithm
Project Meeting January 19th, 2013
Features:
- Analysis window : 1024 samples, 75% overlap, sine window.
- Sampling frequency: 22050 Hz.
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Formant estimation algorithm
Project Meeting January 19th, 2013
Estimation of noise component:
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Formant estimation algorithm
Project Meeting January 19th, 2013
Estimation of formant candidates using LPC and Cepstrum:
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Formant estimation algorithm
Project Meeting January 19th, 2013
Some difficulties:
• The existence of vibrato makes hard the noise estimation
• The closeness between two or more formants
• Finding a rule for the selection and validation the candidates
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Formant estimation algorithm
Project Meeting January 19th, 2013
In SingingStudio:
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Vibrato analysis in Singing Studio
PROJECT MEETING
Ricardo Sousa
Faculdade de Engenharia da Universidade do Porto, Porto
January 19th, 2013
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Introduction
• Context and Objectives
• Singing analysis and bio-feedback applied to singing training/teaching
• Acoustic vibrato parameterization
Robust
Descriptive
Objective
Physiological meaning
• Automatic vibrato detection
• Visual interface of vibrato analysis
Project Meeting January 19th, 2013
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Method
•Analysis Algorithm
Pitch Segment
selection
Vibrato Segment
detection
Parameter
Computation
Pitch Estimation
Voice
Signal
Pitch curve
Pitch
Segments
Vibrato
Segments
Vibrato
Parameters
Step 1
Step 2
Step 3
Step 4
January 19th, 2013 Project Meeting
-Spectral based methods
-Frame based
- FFT interpolation (accurate
F0 estimation)
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Method
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Method
•Vibrato percentage: Ratio of vibrato duration and the entire theme duration
•Mean Duration: Mean duration of all segments.
•Mean frequency: Mean frequency of all segments.
•Mean extension: Mean extension of all segments.
•Sinusoidal Purity: Measure the regularity of vibrato (similarity to sinusoidal waveform)
•Vibrato parameters
Duration
1/Frequency
Extension
January 19th, 2013 Project Meeting
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Results
• Automatic detection of vibrato segments
Vibrato detection 1 Vibrato detection 2
January 19th, 2013 Project Meeting
Additional features:
• Manual adjustment of vibrato segments
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Results
• Qualitative and Quantitative Evaluation
Irregular vibrato Regular vibrato
Observation:
• Qualitative evaluation: “Good vibrato”, “Bad vibrato”
January 19th, 2013 Project Meeting
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Conclusion
• Qualitative and Quantitative evaluation of vibrato.
• Visualization and bio-feedback.
• Automatic analysis.
• Physiological interpretation.
• Interactive interface.
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Truly grateful!
January 19th, 2013 Project Meeting