1. Acoustic impulse response measurement using speech and music signals John Usher Barcelona Media – Innovation Centre | Av. Diagonal, 177, planta 9, 08018 Barcelona

2. John Usher -- In-situ RIR measurement using music and speech 2 Using adaptive filters to estimate acoustic IRs In-situ acquisition of electro-acoustic IR, with audience. Continuous: Fast enough for changing environment conditions. Use speech and music signal radiated from loudspeaker. AF for IR is nothing new! Used for: Acoustic echo and feedback cancellation. Upmixing (2 → 5.1, 2 → 3D). ANC. Room EQ (using noise).

3. John Usher -- In-situ RIR measurement using music and speech 3 Audio source (voice or music) LS AF update Adaptive Filter (AF = h) + _ Mic. error h ~ ∑ Adaptive Filter is updated to model the acoustic IR so that the error signal level (power) is minimized. Basic principle:

4. John Usher -- In-situ RIR measurement using music and speech 4 TD and FD smoothing Homo. Deco. Audio source (voice or music) Filter inversion RIR estimation h EQ filter h ~ Application for room EQ (filtered-x)

5. John Usher -- In-situ RIR measurement using music and speech 5 Localizing objects in a room Emit speech warning from loudspeaker in room. Extract RIR using adaptive filter. Detect reflection onset timing, e.g. using running kurtosis.

6. John Usher -- In-situ RIR measurement using music and speech 6 Application for live sound: De-noising & spatial re-mixing Audio source (voice or music) LS AF update (NLMS) Adaptive Filter (AF = h) + _ Mic. error h ~ ∑ Room signal Audience signal (applause etc.) Clean audio signal (from the desk)

7. John Usher -- In-situ RIR measurement using music and speech 7 Filter update algorithm (NLMS): x(n) LS Update h(n) + _ Mic. e(n) h ~ ∑ 1. 2. y(n)

8. John Usher -- In-situ RIR measurement using music and speech 8 Empirical experiment with small-room configuration Set-up: Single microphone. Single loudspeaker. Small room (RT = 0.5 s). Noise, speech or music radiated. Reference measurement using exponential swept-sine deconvolution. Further test using live (spoken) voice, with close and far lav. mic.

9. John Usher -- In-situ RIR measurement using music and speech 9 Small-room experiment set-up: Audio source (voice or music) Blah blah blah... A. Source is loudspeaker reproducing noise, speech or music. Multichannel noise from loudspeakers. B. Source is live spoken voice. Predict IR between two lav. mics. Lav. 1 Lav. 2 Noise signal (white noise or babble)

10. John Usher -- In-situ RIR measurement using music and speech 10 Results Error Criterion: 1)Start with reference RIR (measured using swept- sine technique). 2)Allow Adaptive Filter to converge for 10 seconds to get AF spectra. Calculate misalignment: mean of difference between the ref. and AF spectra (80 Hz-- 12 kHz):

11. John Usher -- In-situ RIR measurement using music and speech 11 Rate of Convergence

12. John Usher -- In-situ RIR measurement using music and speech 12 Reference RIR from sine-sweep RIR using noise, music, voice (no obvious difference in TD!)

13. John Usher -- In-situ RIR measurement using music and speech 13 Reference RIR from sine-sweep: RIR from live voice and 2 lavs:

14. John Usher -- In-situ RIR measurement using music and speech 14 Comparison of filter spectra using noise, speech and music: (High SNR)

15. John Usher -- In-situ RIR measurement using music and speech 15 Robustness to SNR (25, 12, 3 dB SNR): Masker = noise.

16. John Usher -- In-situ RIR measurement using music and speech 16 Robustness to SNR: Masker = babble

17. John Usher -- In-situ RIR measurement using music and speech 17 Comparison with DCFFT: Dual Channel FFT method: Following AES reviewer recommendation, compared with commercial DCFFT system ( “SMAART”).

18. John Usher -- In-situ RIR measurement using music and speech 18 Comparison of NLMS vs DCFFT:

19. John Usher -- In-situ RIR measurement using music and speech 19 Effectiveness of AF RIR acquisition method with long RIRs. 6 RIRs: Obtained from Dirac fed into Altiverb. (NB: No background noise simulated.) Football stadium, Caen Cathedral, church, EMT plate, Filmorch. Stage Berlin, Castle. RT60: 9.6-1.1 secs. 1.2, 2.3, 3.5, 6.0, 7.8, 9.6.

20. John Usher -- In-situ RIR measurement using music and speech 20 What happens if we just model the early part of the IR? … Not much: most of the spectral detail is in the early part. For longer IRs, the adaptive filter should be longer. Longer RT

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24. John Usher -- In-situ RIR measurement using music and speech 24 Rate of Convergence for different RTs. 340 ms window, 32 x overlap. Longer RT

25. John Usher -- In-situ RIR measurement using music and speech 25 RIR acquisition for small and large rooms : Adaptive filter updated using NLMS and overlapped window. Tested with RT60 = 0.5 -10 secs. Using music, speech and noise as excitation signals. Less accurate using live voice and two mics. Convergence in <3 sec. (<2 dB mean error). Little change in performance with SNRs down to 0 dB. Conclusions:

26. John Usher -- In-situ RIR measurement using music and speech 26 Music vs speech: Music: AF matches RIR 60 Hz—12 kHz. Speech: AF matches RIR 100 Hz– 8 kHz. No considerable improvement for filter sizes >340 ms. I.e. we only need to model first 1/8 th of RIR to have a good approximation of the spectrum. Adaptive whitening algorithm (LPC residuals) can speed up convergence for highly coloured signals, but only in low SNRS. Conclusions:

27. John Usher -- In-situ RIR measurement using music and speech 27 · In-situ continuous room EQ using filtered-x approach. · Object localization using speech message. (e.g. using running kurtosis). · Re-mixing live music: ambient sound separation using filter output and error signal (e.g. get clean signal + room ambiance + audience applause). Applications:

28. John Usher -- In-situ RIR measurement using music and speech 28 Cheers! John Usher

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