Temporal Integration Function of the Human Auditory System, W AUDITION

Transcription

1 Temporal Integration Function of the Human Auditory System, W AUDITION Mg. Ing. Alejandro Bidondo PHD student abidondo@untref.edu.ar 1

2 Mathematical Phisiologycal Model The temporal information is processed by an Autocorrelator. It sets the psichological auditory present. h R e R TyCH R VelMB R spectra Input (x) RR Hemisphere specialization Spatial information R p(t) Onda Sonora Vibración Onda viajera (tonotopía) Conducción neuronal RL Sound source h L e L TyCH L VelMB L spectra Input (x) LL + Temporal information L Sound field. Ear canal. Tímpany & bone chain. Basilar membrane. Hair cells. Coclear Nuclei. Superior Olivar Complex, Lateral Lemniscus. Continuous Cross Correlation. Continuous Autocorrelation. Auditory cortex. Cariani, P., Ando, Y. 2

3 Autocorrelation Function: ACF 1 2T T p' t p' t T dt p Normalized ACF: p p 0 W ACF time window: Is the integration interval. This concept wrongly tends to conceptualize the existence of a time window (instead of a time function). 3

4 Motivation: Which is the duration of the auditory integration window? Is it variable? Could it be a variable function instead of a rectangular window with variable duration? 4

5 Subjetive tests Evaluating the Human Autocorrelation processor: W AutoCorrelationProcessor The detection thresholds of one ear were studied. Panning: Direct signal: panned 100% (totally on one side), Reflection: panned 100% (totally on the same side as the direct signal). 5

6 Subjetive tests Sound Files: 4 Anechoic & monophonic music motiffs. Length: 35 to 50 seconds (each track). Music tracks: a) Sinfonía nro. 8, Iido movement, bars 1-61, from A. Bruckner ( ), made up by : Flutes 1-3, Oboes 1-3, Clarinets 1-3, Bassoons 1-3, French Horns 1-8, Trumpets 1-3, Trombones 1-3, Tuba, Timbal, Violin I (2 sections), Violin II (2 sections), Viola (2 sections), Cello (2 sections), Double-bass (2 sections). b) Michael Jackson vocal track: They don t really care about us, c) Michael Jackson s choir: Do you remember the time and d) Piano part. 6

7 NIVEL SONORO e mín [ms] 1 promedio, [ms] Mean RMS value [db] Panning 0 / 100 Nombre del Sujeto Music motif Acoustic Piano t1 10ms Test 1 Test 2 Test 3 PROMEDIO Echo perception t1 50ms Test 1 Test 2 Test 3 PROMEDIO Echo perception t1 100ms Test 1 Test 2 Test 3 PROMEDIO Echo perception t1 400ms Test 1 Test 2 Test 3 PROMEDIO Echo perception Panning 0 / 50 Acoustic Piano t1 Echo perception 10ms Test 1 Test 2 Test 3 PROMEDIO 0.0 t1 Echo perception 50ms Test 1 Test 2 Test 3 PROMEDIO 0.0 Averaged results: RESULTADOS Delay [ms] Sujeto Media [db] Varianza [db] dB t1 Echo perception 100ms Test 1 Test 2 Test 3 PROMEDIO 0.0 t1 Echo perception 400ms Test 1 Test 2 Test 3 PROMEDIO 0.0 Panning 100 / 100 Acoustic Piano t1 Echo perception 10ms Test 1 Test 2 Test 3 PROMEDIO 0.0 t1 Echo perception 50ms Test 1 Test 2 Test 3 PROMEDIO 0.0 t1 Echo perception 100ms Test 1 Test 2 Test 3 PROMEDIO 0.0 Evaluation List: Each subject completed the green cells with his own detection level of the reflection. Then all green rows were averaged. t1 400ms Test 1 Test 2 Test 3 PROMEDIO Echo perception 0.0 7

8 Instruction 1: Read the instructions carefully INSTRUCTIONS: 1. Calibrate the headphones level to 79dBALeq. 2. Then, don t touch the headphones level during the whole experiment. 3. Stablish the particular essay by MUTING and UNMUTING the appropiate channels. 4. Verify that the fader level of the not delayed track is at 0dB. 5. Hear carefuly and remember the track without any delay. Understand the original track. 6. Set the delay of the particular essay. 7. Set the delayed track panning of the particular essay. 8. Verify that the delayed track level is below -60dB, before initiate the particular essay. 9. Incement the delayed track level until barely perceive it, by means of a gross level adjustement. 10.Vary the delayed track level untill barely perceive it, by means of a fine level adjustement. 11.Register the delayed track fader level in the excell table, particular experiment. 12.Once finalized the 4 sound level detections of each particular essay, change aleatory to the next particular essay. Notes: Do not look at the relative level of the delayed track. You can use MUTE ON / OFF to detect the presence of the delayed track. 8

9 Subjetive tests Experiment s mixing console: It was controlled by each subject under test. Variable reflection level, Relative to the direct signal level. 9

10 Results Panning Direct signal: 100% & Reflected signal: 100%: Resulting detection curves from 10 subjetive tests. 10

11 Results Panning Direct signal: 100% & Reflected signal: 100%: Resulting detection curves from 10 subjetive tests. Curves replaced by Logaritmic (base 10) fitting. 11

12 Panning Direct signal: 100% & Reflected signal: 100%: Reflection detection curves fitting y Fitting the results with a known function: A Log x B Bruckner: A = B = Coef of determination, R-squared = Jackson Vocal: A = B = Coef of determination, R-squared = Piano: A = B = Coef. of determination, R-squared = Jackson Choir: A = B = Coef of determination, R-squared =

13 Effective duration of the ACF for a time period of sound information: What is e? e is the inverse of the information density contained into the sound signal. When e is minimal the informatin density is maximal. At every local eminimum the brain gets sound information. Sound information time period e e is the effective duration of the ACF analyzed in a time period 2T. By moving this period 2T over the sound file, the ACF becomes a running ACF. This way e can be represented graphically over time. 13

14 Autocorrelation Integration window: W ACF W ACF DILEMMA: needs Effective duration of the Autocorrelation Function e But, to get vicious circle But, to get needs W ACF e 14

15 Solving the DILEMMA: Every sound signal has its own and unique information density. Every person applies his own subjetive integration time window. The subjetive time window depends on the sound signal s subjetive information density. So there is: An objetive information density for every signal. A subjetive information density for every signal. A 1000ms Rectangular Temporal Window Proposal: Stablish an integration interval (2T) just for calculus: 1000ms. Then it is possible to obtain the e = f(time) function. t[s] Use the Percentile XX e to obtain the integration function coefficients (A and B slide 18) 15

16 Signal Analysis: using S. Sato s Iterative Method Received at Acta Acustica on 2010 TAU e Music Motiff TAU e min [ms] Percentil 99 Percentil 95 Percentil 90 Bruckner Jackson Choir Jackson Vocal Piano TAU e Time Logging for the 4 studied music motiffs 16

17 Dr. Sato s Method - Tau e Results: A 1000ms Bruckner: Rectangular Temporal Window t[s] Running step = 0.1s Same conceptual results: Robustness Integration interval = 1s. Running step = 0.01s Effective duration of the ACF = TAU e Integration interval = 1s. 17

18 Dr. Sato s Method - Tau e Results: Piano The others 18

19 Dr. Sato s Method - Tau e Results: Statistically the 4 tracks have different behaviours: Piano (Ruby red) Bruckner (Black) Jackson Choir (Red) Jackson Vocal (Blue) ***Complexity at the design stage of real sound stimuly with special ACF characteristics. 19

20 W Audition [ms] Is the time window shape Rectangular, Gaussian, RoundedExponential, or??? Which could be the best overlap / running step between time windows? 20

21 WACF: ACF Subjetive Time Window Reflection delay: t y A Log x B Are they in function of the (Percentile 99% ( e ))? By using the curve fitting from slide 12, these graphs are obtained. 21

22 WACF: ACF Subjetive Time Function Relationship between TAU e and A & B Parameters Music Motiff y = A. LOG10(x) + B Percentile 99 Percentile 95 Percentile 90 (TAUe) (TAU e) (TAU e) A B Piano Bruckner Jackson Choir Jackson Vocal

23 y A Log x B A Parameter Function curve fitting for the A Parameter in function of Percentile 99% of TAU e. Log Equation fitting: Y = * ln(x) Coef of determination: R-squared = Residual mean square: Sigma-hat-sq'd = Percentile 99%_ A Ln e 23

24 y A Log x B B Parameter Function curve fitting for the B Parameter in function of Percentile 99% of TAU e. Linear Equation fitting: Y = * X Coef. of determination: R-squared = Residual mean square: Sigma-hat-sq'd = Percentile 99%_ B e 24

25 WACF: ACF Subjetive Time window Function Percentile 99%_ A Ln e Percentile 99%_ B e Att[ db] A Log t B Att[ linear ] t A B 10 t :[ ms] Integration Time Function of the Human Auditory system. t [ms]: future time, starting from the direct signal perception. 25

26 WACF: ACF Subjetive Time window Function With longer integration intervals the fitting curves were not as good as with 2T = 1second. With shorter integration intervals the Regression coefficient of SATO s Iteartion Method (for finding TAU e) registered values below 0.85 (not enough confident results). Many thanks to Dr. Sato for his collaboration. TAU e calculation method is of vital importance to obtain the Temporal Integration Window function. 26

27 racf Running Autocorrelation Function Music Motiff Temporal Window 2T What shape? Histogram e Detection of e W Audition = W ACF ACF This graph depicts a model of how the integration time function of our auditory system works. e : ten-percentile delay of the envelope of the normalized ACF D Orazio, D; Garai, M. (2009). Some considerations about the integration of r-acf. 4 th International Symposium on Temporal Design Kumamoto University,

28 WACF: ACF Subjetive Time window Function New proposal: 0dB 10ms Log Time [s] -5.3dB Average curve s beginnings from slide 18. y A Log x B A, B = f [Percentile 99% ( e )] -60dB Proposed variable time integration function, in function of A and B parameters. 28

29 WACF: ACF Subjetive Time window Function Sound file Example of the proposed model of variable integration time function, working over a sound file. Adaptive time window Function from slide 22 Running step: 10ms Log Time [s] 29

30 WACF: ACF Subjetive Time window Function Att[ db] Att[ linear ] t t :[ ms] A Log A 10 t 10 B B 10 Percentile 99%_ A Ln Percentile 99%_ B e e Temporal Auditory Integration Function shape of the human hearing system for inespecific values of A and B. The upper figure shows the vertical axis in db, while the lower figure shows the vertical axis in linear units. Horizontal axis in seconds. 30

31 Short Time Memory [Naatanen, 1996] 10ms 31

32 Conclusions: There is no such a early and late response. Nor fixed. Sound level metering may be rectified. Each music, each performance can (should) be adapted to each hall acoustics. The auditory time function varies with a signal s property: emin. Prediction of W ACF implies rectification of some acoustic parameters. Is there a standard neural bit rate so for low information signals the integration function is large, and for high information signals is short? 32

33 Example of use: Concert Hall IR 33

34 W Audition Usefullness Concert hall Impulse response; 2T = 150ms Perceived decay 34

35 W Audition Usefullness Concert hall Impulse response; 2T = 30ms Perceived decay 35

36 Special thanks to: Prof. Dr. Yoichi Ando. Prof. Dr. Shinichi Sato. Student Facundo Ramón. Student Nicolás García. UNTREF University. 36

37 Thank you! Mg. Ing. Alejandro Bidondo PHD student UNTREF Buenos Aires, ARGENTINA. 37