Surrounded by High-Definition Sound

Transcription

1 Surrounded by High-Definition Sound Dr. ChingShun Lin CSIE, NCU May 6th, 009

2 Introduction What is noise? Uncertain filters

3 Introduction (Cont.) How loud is loud? (Audible: 0Hz - 0kHz)

4 Introduction (Cont.) Major difference between speech processing and audio processing. Precision does matter. channels and.1 channels systems.

5 Introduction (Cont.) Tips for loudspeakers placement (something you probably know). The longer distance among listener and speakers, the more spatial sound you hear. The nearer distance among listener and speakers, the more direct sound you hear. The closer you place a speaker to intersecting room surfaces (corners, wall and ceiling, wall and floor), the stronger the bass output.

6 Introduction (Cont.) Tips for loudspeakers placement (Cont.). Tweeters should be positioned at ear level. No specific rule for placing the subwoofer; however, the amount of bass may vary depending on room location. Avoid bare surfaces. Add carpeting and drapes. Avoid room with square dimension or has one dimension exactly twice another. If your stereo speakers are not shielded, don't place them too close to the TV.

7 Introduction (Cont.) 5.1 channel, 7.1 channel, and 10. channel systems. 5.1 channels: surrounding loudspeakers, more reflection is needed. 7.1 channels: 4 surrounding loudspeakers. Both of these processes add surrounding information for greater realism and more dramatic effects.

8 Introduction (Cont.) 5.1 channels, 7.1 channels, and 10. channels systems (Cont.). 10. channels: More immersive.

9 Introduction (Cont.) 5.1 channels, 7.1 channels, and 10. channels systems (Cont.). Spatialization by azimuth and elevation cues. A typical ambiophonics array (frontal stereo dipole, plus 8-loudspeakers surround rig). Hybrid solution, aimed to mask the defects of two basic systems: cross-talk cancelled reproduction of binaural material over closelyspaced loudspeakers (Stereo Dipole) and 3D surround driven by convolution of corresponding oriented virtual microphones.

10 Introduction (Cont.) Term definition. ITD (Interaural time difference) ITD approximation based on distance difference of a sound arriving at the two ears.

11 Introduction (Cont.) Term definition. ITD (Interaural time difference) (Cont.) Measured and simulated ITDs at various elevation angles as a function of azimuth angle.

12 Introduction (Cont.) Term definition. ILD (Interaural level difference) In spatial hearing, it is well known that low-frequency (below approximately 1.5 khz) interaural time difference (ITD) cures have a dominant role in location, whereas the interaural level difference (ILD) is the major localization cue at higher frequencies.

13 Introduction (Cont.) Term definition. ILD (Interaural level difference) (Cont.) The effective frequency range of the interaural pressure level difference (ILD) spans throughout the audible frequency range, although at low frequencies the role of the ITD is dominant. Problems in the localization of sound occur mostly in the median plane, where the ITD and ILD are small or zero, and on the cone of confusion, where ITD and ILD cues are ambiguous.

14 Introduction (Cont.) The use of non-individualized transfer functions leads to a degradation of localization accuracy, increasing the following errors: 1. Localization error, referring to the deviation of the perceived to the synthesized direction of a virtual sound source.. Localization blur, that describes the width of the perceived stimulus. 3. Externalization error, also termed as inside-the-head localization. 4. Cone of confusion, which refers to localization errors caused by contours of constant interaural time difference (ITD) and interaural level difference (ILD) resulting in front-back confusions.

15 Introduction (Cont.) Term definition. Equal loudness curves. Equal loudness contours from Robinson and Dadson, now ISO standard.

16 Reverberation time (Cont.) Measurement of reverberation time 1. The chief problem is the accurate measurement of very small times which may be as little as 0.5s.. A sound is produced of suitable amplitude and then the rate of decay of its different frequencies is measured. 3. The level recorder will need to have a logarithmic potentiometer in the circuit to convert the pressure measurement into db. 4. Ideally, the source of sound will be octave or third-octave band random noise from a whitenoise generator, through an amplifier to a pair of loudspeakers.

17 Reverberation time (Cont.) 3. Measurement of reverberation time (Cont.): Y ( z) = H ( z) X ( z) y( n) = h( n) x( n) To achieve: Y ( z) = H ( z) X( z) = 1 x( n) = δ ( n) In each case measurements will usually be made in octave bandwidths, whose center frequencies are 15 Hz, 50 Hz, 500 Hz, 1000 Hz, 000 Hz, and 4000 Hz. Or in third-octave bandwidths, those may be done by switching the filters in the frequency analyzer.

18 Room acoustics/ equalization Reflection-free zone. Using strategically placed absorbers, diffusers, and bass traps can help control unwanted room reflections.

19 Room acoustics/ equalization (Cont.) Absorption and reflection in relation to temporal and spatial responses.

20 Room acoustics/ equalization (Cont.) Diffusion in relation to temporal and spatial responses. Abffusor, omniffusor, and B.A.S.S. Trap.

21 Room acoustics/ equalization (Cont.) Diffusing effect of convex surfaces and focusing effect of concave surfaces. Confusion of audio delivery. Reverberation time estimation using impulse response. Live theater vs. Dead theater. Wet concert hall vs. Dry concert hall.

22 Room acoustics/ equalization (Cont.) Make the walls a few millimeters out of parallel to avoid flutter echoes. Correctly shaped ceiling or ceiling reflector can provide uniform distribution of sound energy. Use acoustical foams

23 Room acoustics/ equalization (Cont.) Measurement in the laboratory. CMU00 HATS Type3.3 HP89 UPL16

24 Room acoustics/ equalization (Cont. modeling) Example: Consider a simple first-order specular room reflection modeled as: h ( n) = δ ( n) + α δ ( n 1); h 1 ( n) = δ ( n) + β δ ( n 1); α β < 1 α Two position are located along the same radius from a source, and each position has a differently absorbing neighboring wall with negligible higher-order reflections from each wall. Ideal equalization at position 1 is attached if the equalizing filter, h eq (n), is: h eq n ( α ) u( ) ( n) = n Then we obtain: h eq ( n) h1 ( n) = δ ( n) where u( n) = 1, n 0 is the discrete step function.

25 Room acoustics/ equalization (Cont. modeling) Example (Cont.): However, for the h eq h ( n ) Time domain error function: n 1 ( α β )( α ) u( n 1) ( n) h ( n) = δ ( n) 1 ε = I 1 = I I 1 n= 0 I 1 ( δ ( n) heq ( n) h ( n) ) n= 0 e ( n) ( α β) = I I 1 n= 1 ( α ) (n ) Clearly, the response at position is unequalized because ε > 0. To get a smaller error, a good equalizer that considers the variations in the source and listening positions is necessary.

26 Room acoustics/ equalization (Cont. modeling) Example (Cont.):

27 Room acoustics/ equalization (Cont. modeling) Multi-EQ. 1. Calculates a room correction solution for each channel.. Based on the complex mathematics of pattern recognition and fuzzy logic. 3. A room response, h j, can strictly belong to one and only one cluster. u ( h ) {0,1} u ( h ) [0,1] i j 4. Fuzzy C-means clustering: Direct path components for responses 1, 3. Reflection path components for responses, 3. i j Therefore, it s clear that if responses at 1, are clustered separately into two different clusters, response at 3 should belong to both clusters to some degree.

28 Room acoustics/ equalization (Cont. modeling) Multi-EQ (Cont.). The centroids and membership functions are determined by: M ( ui ( hk )) hk u hˆ i * k = 1 j = M k = 1 ( u ( h )) i k ˆ * where dik = hk hi, i = 1,, L, c; k = 1,, L, M, and j denotes the i-th cluster room response centroid. And then the single final prototype can be combined from several minimum-phase responses: h ( h final j ) 1 c d ik = ( ) = c j= 1 d jk c ( = j= 1 k= 1 c M ( j= 1 M ( u ( h k = 1 i i k ( u ( h )) k )) ) hˆ ) j= 1 * j 1 dik 1 d jk h ˆ *

29 Immersive audio rendering over loudspeakers (Cont.) Rendering filters for a single listener: where W is the matrix of the crosstalk canceller. The rendering system transfer function T is: Our goal: where W requires four weight vectors to produce the desired signal.

30 Immersive audio rendering over loudspeakers (Cont.) Time domain adaptive inverse control filter: The above equation can be rewritten as: where the diagonal elements are the ipsilateral transfer functions, while the off-diagonal elements are the contralateral transfer functions. Using the LMS adaptive algorithm, the weight vectors are updated as: The positive scalar step size µ controls the convergence rate and steadystage performance of the algorithm.

31 Immersive audio rendering over loudspeakers (Cont.) Time domain adaptive inverse control filter (Cont.): The gradient estimate, ˆ ( n ), is simply the derivative of respect to W(n). e ( n ) > 0 with The output error is given by where

32 Immersive audio rendering over loudspeakers (Cont.) Time domain adaptive inverse control filter (Cont.): The following figure shows the LMS block diagram for the estimation of the crosstalk cancellation filter with d ( n) X ( n m) and for the left and right channels, respectively. i = d ( n) = X ( n m) L i R

33 Immersive audio rendering over loudspeakers (Cont.) Frequency domain adaptive inverse control filter: The general form of FDAFLMS algorithms can be expressed as follows: where the superscript H denotes the complex conjugate transpose. The time-varying matrix µ(k) is diagonal and it contains the step sizes µ 1 ( k). In general, each step size is varied according to the signal power in that frequency bin l. where µ is a fixed scalar and S i (k) is a product of input signal X L or X R. P i (k) is an estimation of the signal power in the n-th-input signal where λ =1 α is a forgetting factor. P i (k) and S i (k) are vectors composed of N different bins.

34 Immersive audio rendering over loudspeakers (Cont.) Frequency domain adaptive inverse control filter (Cont.): The following figure shows the frequency domain adaptive LMS inverse algorithm block diagram using overlap-save method for the estimation of crosstalk canceller weighting vectors.

35 Immersive audio rendering over loudspeakers (Cont.) Rendering filters for multiple listeners nonsymmetric case: where the ear signal matrix, the loudspeaker signal matrix, and the input binaural signal matrix. where T is HRTF matrix, and W is crosstalk cancellation filter.

36 Immersive audio rendering over loudspeakers (Cont.) Rendering filters for multiple listeners nonsymmetric case (Cont.): Our goal: Equation manipulation: ' [ ] T [ ] T where = WW W W W W W W, and X = X X X X. W L R L R

37 Immersive audio rendering over loudspeakers (Cont.) Rendering filters for multiple listeners nonsymmetric case (Cont.): Based on LMS adaptive algorithm, the weight vectors are updated: The output errors: where The filter output:

38 Immersive audio rendering over loudspeakers (Cont.) Rendering filters for multiple listeners nonsymmetric case (Cont.): The following figure shows the LMS block diagram for the estimation of crosstalk canceller weighting vectors in the general nonsymmetric case, with d ( n) = X ( n m) and d ( n) = X ( n m) respectively. i L i R for the left and right cannels,

39 Immersive audio rendering over loudspeakers (Cont.) Frequency response of crosstalk canceller adapted in the time domain: (a) Magnitude response of (b) Magnitude response of (c) Phase response of T 1( ω) W1 ( ω) + T ( ω) W ( ω) (Ipsilateral signal) T 1( ω) W3 ( ω) + T ( ω) W4 ( ω) (Contralateral signal) T 1( ω) W1 ( ω) + T ( ω) W ( ω)

40 Conclusion Hearing is believing. Any question?