Spectrum Analysis Sound AnalysisSound Analysis What are we going to do?What are we going to do? Record a soundRecord a sound recorded sound analog-to-digital.

Slides:



Advertisements
Similar presentations
Acoustic/Prosodic Features
Advertisements

Why is the amplitude spectrum important?
Department of Kinesiology and Applied Physiology Spectrum Estimation W. Rose
MUSIC NOTES Noise Versus Music  What is the difference between noise and music?  Answer: The appearance of the waveform.  What is the difference between.
Easily extensible unix software for spectral analysis, display modification, and synthesis of musical sounds James W. Beauchamp School of Music Dept.
CMPS1371 Introduction to Computing for Engineers PROCESSING SOUNDS.
Physics 1251 The Science and Technology of Musical Sound Unit 1 Session 8 Harmonic Series Unit 1 Session 8 Harmonic Series.
DFT/FFT and Wavelets ● Additive Synthesis demonstration (wave addition) ● Standard Definitions ● Computing the DFT and FFT ● Sine and cosine wave multiplication.
EE2F2 - Music Technology 9. Additive Synthesis & Digital Techniques.
Harmonic Series and Spectrograms 220 Hz (A3) Why do they sound different? Instrument 1 Instrument 2Sine Wave.
Note To be transmitted, data must be transformed to electromagnetic signals.
Han Q Le© ECE 3336 Introduction to Circuits & Electronics Lecture Set #10 Signal Analysis & Processing – Frequency Response & Filters Dr. Han Le ECE Dept.
The frequency spectrum
Page 0 of 34 MBE Vocoder. Page 1 of 34 Outline Introduction to vocoders MBE vocoder –MBE Parameters –Parameter estimation –Analysis and synthesis algorithm.
A.Diederich– International University Bremen – USC – MMM Spring Sound waves cont'd –Goldstein, pp. 331 – 339 –Cook, Chapter 7.
Spectrum Analyzer. Another Oscilloscope??? Like an oscilloscope Oscilloscope in time domain Spectrum analyzer in frequency domain (selectable)
Dr. Jie ZouPHY Chapter 8 (Hall) Sound Spectra.
Additive Synthesis Any periodic waveform can be expressed as the sum of one or more sine wavesAny periodic waveform can be expressed as the sum of one.
Harmonics and Overtones Waveforms / Wave Interaction Phase Concepts / Comb Filtering Beat Frequencies / Noise AUD202 Audio and Acoustics Theory.
PH 105 Dr. Cecilia Vogel Lecture 12. OUTLINE  Timbre review  Spectrum  Fourier Synthesis  harmonics and periodicity  Fourier Analysis  Timbre and.
Square wave Fourier Analysis + + = Adding sines with multiple frequencies we can reproduce ANY shape.
The Spectrum Jean Baptiste Fourier ( ) discovered a fundamental tenet of wave theory All periodic waves are composed of a series of sinusoidal.
The Spectrum n Jean Baptiste Fourier ( ) discovered a fundamental tenet of wave theory n All periodic waves are composed of a series of sinusoidal.
Human Psychoacoustics shows ‘tuning’ for frequencies of speech If a tree falls in the forest and no one is there to hear it, will it make a sound?
Leakage & Hanning Windows
Harmonics, Timbre & The Frequency Domain
infinity-project.org Engineering education for today’s classroom 53 Design Problem - Digital Band Build a digital system that can create music of any.
Where we’re going Speed, Storage Issues Frequency Space.
Using Technology and Music to Talk About Sinusoids Mike Thayer Summit High School, Summit, NJ 25 th Annual “Good Ideas in Teaching Precalculus And…” Conference.
Chapter-4 Synthesis and Analysis of Complex Waves Fourier Synthesis: The process of combining harmonics to form a complex wave. Fourier Analysis: Determining.
© Oxford University Press b Harmonics A tuning fork produces a note with only one frequency. The shape of the wave on the oscilloscope is very.
ACOUSTICS AND THE ELEMENTS OF MUSIC Is your name and today’s date at the top of the worksheet now?
Beats and Tuning Pitch recognition Physics of Music PHY103.
Harmonic Series and Spectrograms
Fourier series. The frequency domain It is sometimes preferable to work in the frequency domain rather than time –Some mathematical operations are easier.
Wireless and Mobile Computing Transmission Fundamentals Lecture 2.
CH. 21 Musical Sounds. Musical Tones have three main characteristics 1)Pitch 2) Loudness 3)Quality.
Why does a violin sound different from a horn? Several kinds of audible information Pitch Timbre Attack Decay Vibrato.
Complex Auditory Stimuli
Pre-Class Music Paul Lansky Six Fantasies on a Poem by Thomas Campion.
Physics 1251 The Science and Technology of Musical Sound Unit 1 Session 7 Good Vibrations Unit 1 Session 7 Good Vibrations.
Fourier Series Fourier Transform Discrete Fourier Transform ISAT 300 Instrumentation and Measurement Spring 2000.
Chapter 21 Musical Sounds.
Harmonic Series and Spectrograms BY JORDAN KEARNS (W&L ‘14) & JON ERICKSON (STILL HERE )
CHAPTER 4 COMPLEX STIMULI. Types of Sounds So far we’ve talked a lot about sine waves =periodic =energy at one frequency But, not all sounds are like.
The Physics of Music Waves
3.3 Waves and Stuff Science of Music 2007 Last Time  Dr. Koons talked about consonance and beats.  Let’s take a quick look & listen at what this means.
The Spectrum n Jean Baptiste Fourier ( ) discovered a fundamental tenet of wave theory.
Basic Acoustics. Sound – your ears’ response to vibrations in the air. Sound waves are three dimensional traveling in all directions. Think of dropping.
 Wave energy depends on amplitude, the more amplitude it has, the more energy it has.
Design of a Guitar Tab Player in MATLAB Summary Lecture Module 1: Modeling a Guitar Signal.
And application to estimating the left-hand fingering (automatic tabulature generation) Caroline Traube Center for Computer Research in Music and Acoustics.
Intro to Fourier Series BY JORDAN KEARNS (W&L ‘14) & JON ERICKSON (STILL HERE )
Complex Form of Fourier Series For a real periodic function f(t) with period T, fundamental frequency where is the “complex amplitude spectrum”.
Loudness level (phon) An equal-loudness contour is a measure of sound pressure (dB SPL), over the frequency spectrum, for which a listener perceives a.
CS 591 S1 – Computational Audio
Spectrum Analysis and Processing
Pitch.
Figure Hz sine wave to be sampled.
Intro to Fourier Series
Why is the amplitude spectrum important?
Speech Pathologist #10.
Lab 6: Sound Analysis Fourier Synthesis Fourier Analysis
Introduction to Csound 5.
Physical Layer Part 1 Lecture -3.
Wavetable Synthesis.
Hanning and Rectangular Windows
Data Processing Chapter 3
Introduction to Csound 2.
Geol 491: Spectral Analysis
Presentation transcript:

Spectrum Analysis Sound AnalysisSound Analysis What are we going to do?What are we going to do? Record a soundRecord a sound recorded sound analog-to-digital converter samples time-varying Fourier Analysis amplitudes and phases Analyze the soundAnalyze the sound Additive Synthesis resynthesized sound Resynthesize the soundResynthesize the sound Play a musical selection demonstrating the instrument designPlay a musical selection demonstrating the instrument design Prepare the soundPrepare the sound

Spectrum Analysis soundfile.wav PC.wav-format soundfile pvan.exe soundfile.pvn interactive program for spectrum analysis analysis file with amplitudes and frequencies pvan.exe graphs of spectra interactive program for spectrum display

Synthetic Trumpet Real musical instruments produce almost-harmonic soundsReal musical instruments produce almost-harmonic sounds The waveform of this synthetic trumpet repeats more exactly than that of a real instrumentThe waveform of this synthetic trumpet repeats more exactly than that of a real instrument

Spectrum of a Sound For any periodic waveform, we can find the spectrum of the waveform.For any periodic waveform, we can find the spectrum of the waveform. The spectrum is the relative amplitudes of the harmonics that make up the waveform.The spectrum is the relative amplitudes of the harmonics that make up the waveform. The plural form of the word "spectrum" is "spectra."The plural form of the word "spectrum" is "spectra."

Spectrum of a Sound Example: amp1 = 1, amp2 =.5, and amp3 =.25, the spectrum = {1,.5,.25}.Example: amp1 = 1, amp2 =.5, and amp3 =.25, the spectrum = {1,.5,.25}. The following graphs show the usual ways to represent the spectrum:The following graphs show the usual ways to represent the spectrum: FrequencyHarmonic Number

Finding the Spectrum of a Sound 1.isolate one period of the waveform 2.Discrete Fourier Transform of the period. These steps together are called spectrum analysis.These steps together are called spectrum analysis.

Time-Varying Fourier Analysis User specifies the fundamental frequency for ONE toneUser specifies the fundamental frequency for ONE tone Automatically finding the fundamental frequency is called pitch tracking — a current research problemAutomatically finding the fundamental frequency is called pitch tracking — a current research problem For example, for middle C:For example, for middle C: f 1 =261.6 sound time-varying Fourier Analysis Fourier Coefficients Math amplitudes and phases

Time-Varying Fourier Analysis Construct a window function that spans two periods of the waveform.Construct a window function that spans two periods of the waveform. The most commonly used windows are called Rectangular (basically no window), Hamming, Hanning, Kaiser and Blackman.The most commonly used windows are called Rectangular (basically no window), Hamming, Hanning, Kaiser and Blackman. Except for the Rectangular window, most look like half a period of a sine wave:Except for the Rectangular window, most look like half a period of a sine wave:

Time-Varying Fourier Analysis The window function isolates the samples of two periods so we can find the spectrum of the sound.The window function isolates the samples of two periods so we can find the spectrum of the sound.

Time-Varying Fourier Analysis The window function will smooth samples at the window endpoints to correct the inaccurate user- specified fundamental frequency.The window function will smooth samples at the window endpoints to correct the inaccurate user- specified fundamental frequency. For example, if the user estimates f 1 =261.6, but it really is 259 Hz.For example, if the user estimates f 1 =261.6, but it really is 259 Hz.

Time-Varying Fourier Analysis Samples are only non-zero in windowed region, and windowed samples are zero at endpoints.Samples are only non-zero in windowed region, and windowed samples are zero at endpoints.

Time-Varying Fourier Analysis Apply window and Fourier Transform to successive blocks of windowed samples.Apply window and Fourier Transform to successive blocks of windowed samples. Slide blocks one period each time.Slide blocks one period each time.

Spectrum Analysis We analyze the tone (using the Fourier transform) to find out the strength of the harmonic partialsWe analyze the tone (using the Fourier transform) to find out the strength of the harmonic partials Here is a snapshot of a [i:37] trumpet tone one second after the start of the toneHere is a snapshot of a [i:37] trumpet tone one second after the start of the tone

Trumpet's First Harmonic The trumpet's first harmonic fades in and out as shown in this amplitude envelope:The trumpet's first harmonic fades in and out as shown in this amplitude envelope:

Spectral Plot of Trumpet's First 20 Harmonics

Spectra of Other Instruments [i:38] English horn:[i:38] English horn: pitch is E3, Hertz

Spectra of Other Instruments [i:39] tenor voice:[i:39] tenor voice: pitch is G3, 192 Hertz

Spectra of Other Instruments [i:40] guitar:[i:40] guitar: pitch is A2, 110 Hertz

Spectra of Other Instruments [i:41] pipa:[i:41] pipa: pitch is G2, 98 Hertz

Spectra of Other Instruments [i:42] cello:[i:42] cello: pitch is Ab3, 208 Hertz

Spectra of Other Instruments [i:43] E-mu's synthesized cello:[i:43] E-mu's synthesized cello: pitch is G2, 98 Hertz