Frequency analysis.

Slides:



Advertisements
Similar presentations
Decibel values: sum and difference. Sound level summation in dB (1): Incoherent (energetic) sum of two different sounds: Lp 1 = 10 log (p 1 /p rif ) 2.
Advertisements

| Page Angelo Farina UNIPR | All Rights Reserved | Confidential Digital sound processing Convolution Digital Filters FFT.
Angelo Farina Dip. di Ingegneria Industriale - Università di Parma Parco Area delle Scienze 181/A, Parma – Italy
Angelo Farina Dip. di Ingegneria Industriale - Università di Parma Parco Area delle Scienze 181/A, Parma – Italy
DCSP-12 Jianfeng Feng
Acoustic/Prosodic Features
Digital Audio Compression
ACHIZITIA IN TIMP REAL A SEMNALELOR. Three frames of a sampled time domain signal. The Fast Fourier Transform (FFT) is the heart of the real-time spectrum.
1 Chapter 16 Fourier Analysis with MATLAB Fourier analysis is the process of representing a function in terms of sinusoidal components. It is widely employed.
Hearing and Deafness 2. Ear as a frequency analyzer Chris Darwin.
CS 551/651: Structure of Spoken Language Lecture 11: Overview of Sound Perception, Part II John-Paul Hosom Fall 2010.
2004 COMP.DSP CONFERENCE Survey of Noise Reduction Techniques Maurice Givens.
DFT Filter Banks Steven Liddell Prof. Justin Jonas.
A.Diederich– International University Bremen – USC – MMM Spring Sound waves cont'd –Goldstein, pp. 331 – 339 –Cook, Chapter 7.
1 Audio Compression Techniques MUMT 611, January 2005 Assignment 2 Paul Kolesnik.
Time and Frequency Representations Accompanying presentation Kenan Gençol presented in the course Signal Transformations instructed by Prof.Dr. Ömer Nezih.
SUBJECTIVE ATTRIBUTES OF SOUND Acoustics of Concert Halls and Rooms Science of Sound, Chapters 5,6,7 Loudness, Timbre.
JF 12/04111 BSC Data Acquisition and Control Data Representation Computers use base 2, instead of base 10: Internally, information is represented by binary.
Short Time Fourier Transform (STFT)
EE2F1 Speech & Audio Technology Sept. 26, 2002 SLIDE 1 THE UNIVERSITY OF BIRMINGHAM ELECTRONIC, ELECTRICAL & COMPUTER ENGINEERING Digital Systems & Vision.
SPPA 403 Speech Science1 Unit 3 outline The Vocal Tract (VT) Source-Filter Theory of Speech Production Capturing Speech Dynamics The Vowels The Diphthongs.
EE2F1 Speech & Audio Technology Sept. 26, 2002 SLIDE 1 THE UNIVERSITY OF BIRMINGHAM ELECTRONIC, ELECTRICAL & COMPUTER ENGINEERING Digital Systems & Vision.
Basic Concepts: Physics 1/25/00. Sound Sound= physical energy transmitted through the air Acoustics: Study of the physics of sound Psychoacoustics: Psychological.
T Digital Signal Processing and Filtering
Self-Calibrating Audio Signal Equalization Greg Burns Wade Lindsey Kevin McLanahan Jack Samet.
Ni.com Data Analysis: Time and Frequency Domain. ni.com Typical Data Acquisition System.
Physics 371 March 7, 2002 Consonance /Dissonance Interval = frequency ratio Consonance and Dissonance Dissonance curve The Just Scale major triad construction.
Beats and Tuning Pitch recognition Physics of Music PHY103.
R ESEARCH BY E LAINE C HEW AND C HING -H UA C HUAN U NIVERSITY OF S OUTHERN C ALIFORNIA P RESENTATION BY S EAN S WEENEY D IGI P EN I NSTITUTE OF T ECHNOLOGY.
Intensity, Intensity Level, and Intensity Spectrum Level
Multiresolution STFT for Analysis and Processing of Audio
1 of 20 Z. Nikolova, V. Poulkov, G. Iliev, G. Stoyanov NARROWBAND INTERFERENCE CANCELLATION IN MULTIBAND OFDM SYSTEMS Dept. of Telecommunications Technical.
Dual-Channel FFT Analysis: A Presentation Prepared for Syn-Aud-Con: Test and Measurement Seminars Louisville, KY Aug , 2002.
Acoustic Analysis of Speech Robert A. Prosek, Ph.D. CSD 301 Robert A. Prosek, Ph.D. CSD 301.
Wireless and Mobile Computing Transmission Fundamentals Lecture 2.
1 Speech and Audio Processing and Coding (cont.) Dr Wenwu Wang Centre for Vision Speech and Signal Processing Department of Electronic Engineering
By Sarita Jondhale1 Signal Processing And Analysis Methods For Speech Recognition.
Basics of Neural Networks Neural Network Topologies.
CH. 21 Musical Sounds. Musical Tones have three main characteristics 1)Pitch 2) Loudness 3)Quality.
Angelo Farina Dip. di Ingegneria Industriale - Università di Parma Parco Area delle Scienze 181/A, Parma – Italy
Wavelet transform Wavelet transform is a relatively new concept (about 10 more years old) First of all, why do we need a transform, or what is a transform.
Speech Signal Representations I Seminar Speech Recognition 2002 F.R. Verhage.
Gammachirp Auditory Filter
BA , 1 Basic Frequency Analysis of Sound Contents: Frequency and Wavelength Frequency Analysis Perception of Sound.
Submitted By: Santosh Kumar Yadav (111432) M.E. Modular(2011) Under the Supervision of: Mrs. Shano Solanki Assistant Professor, C.S.E NITTTR, Chandigarh.
LPC-analysis-VOSIM-resynthesis Combined class December 18 th 2012 Johan & Peter Institute of Sonology Royal Conservatory, The Hague.
The Ear As a Frequency Analyzer Reinier Plomp, 1976.
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.
Fourier and Wavelet Transformations Michael J. Watts
The Speech Chain (Denes & Pinson, 1993)
Signal Analyzers. Introduction In the first 14 chapters we discussed measurement techniques in the time domain, that is, measurement of parameters that.
The Spectrum n Jean Baptiste Fourier ( ) discovered a fundamental tenet of wave theory.
Fletcher’s band-widening experiment (1940) Present a pure tone in the presence of a broadband noise. Present a pure tone in the presence of a broadband.
Short Time Fourier Transform (STFT) CS474/674 – Prof. Bebis.
Fletcher’s band-widening experiment (1940)
CS 591 S1 – Computational Audio
Two Vacuums Shopvac Bosch Dept. of Mech. Engineering 1
A map of periodicity orthogonal to frequency representation in the cat auditory cortex.  Gerald Langner, Ben Godde, and Hubert R. Dinse Examples of auditory.
Fourier and Wavelet Transformations
ACOUSTICS part – 3 Sound Engineering Course
Wavelet transform Wavelet transform is a relatively new concept (about 10 more years old) First of all, why do we need a transform, or what is a transform.
Fourier Analyses Time series Sampling interval Total period
Sound shadow effect Depends on the size of the obstructing object and the wavelength of the sound. If comparable: Then sound shadow occurs. I:\users\mnshriv\3032.
Lecture 2: Frequency & Time Domains presented by David Shires
Noise Aperiodic complex wave
Uses of filters To remove unwanted components in a signal
Govt. Polytechnic Dhangar(Fatehabad)
Electrical Communication Systems ECE Spring 2019
Electrical Communications Systems ECE
Electrical Communications Systems ECE
Presentation transcript:

Frequency analysis

The sound spectrum is a chart of SPL vs frequency. Simple tones have spectra composed by just a small number of “spectral lines”, whilst complex sounds usually have a “continuous spectrum”. Pure tone Musical sound Wide-band noise “White noise”

Time-domain waveform and spectrum: Sinusoidal waveform Periodic waveform Random waveform

Analisi in bande di frequenza: A practical way of measuring a sound spectrum consist in employing a filter bank, which decomposes the original signal in a number of frequency bands. Each band is defined by two corner frequencies, named higher frequency fhi and lower frequency flo. Their difference is called the bandwidth Df. Two types of filterbanks are commonly employed for frequency analysis: constant bandwidth (FFT); constant percentage bandwidth (1/1 or 1/3 of octave).

Constant bandwidth analysis: “narrow band”, constant bandwidth filterbank: f = fhi – flo = constant, for example 1 Hz, 10 Hz, etc. Provides a very sharp frequency resolution (thousands of bands), which makes it possible to detect very narrow pure tones and get their exact frequency. It is performed efficiently on a digital computer by means of a well known algorithm, called FFT (Fast Fourier Transform)

Constant percentage bandwidth analysis: Also called “octave band analysis” The bandwidth Df is a constant ratio of the center frequency of each band, which is defined as: fhi = 2 flo 1/1 octave   fhi= 2 1/3 flo 1/3 octave Widely employed for noise measurments. Typical filterbanks comprise 10 filters (octaves) or 30 filters (third-octaves), implemented with analog circuits or, nowadays, with IIR filters

Nominal frequencies for octave and 1/3 octave bands:

Octave and 1/3 octave spectra: 1/3 octave bands 1/1 octave bands

Logaritmic frequency axis Narrowband spectra: Linear frequency axis Logaritmic frequency axis

White noise and pink noise Flat in a narrowband analysis Pink Noise: flat in octave or 1/3 octave analysis

Critical Bands (BARK): The Bark scale is a psychoacoustical scale proposed by Eberhard Zwicker in 1961. It is named after Heinrich Barkhausen who proposed the first subjective measurements of loudness Third octave bands

Critical Bands (BARK): Comparing the bandwidth of Barks and 1/3 octave bands Barks 1/3 octave bands