2. 1 Reconfigurable Acceleration of Microphone Array Algorithms for Speech Enhancement Ka Fai Cedric Yiu, Yao Lu, Xiaoxiang Shi The Hong Kong Polytechnic University Chun Hok Ho, Wayne Luk Imperial College London Nedelko Grbric Blekinge Institute of Technology 3 Jul 2008ASAP 2008

3. 2 Contributions 1. FPGA-based hardware architecture for acoustic beamformer  calibrated signals  subband processing 2. Bitwidth analysis to explore suitable bitwidth of the system 3. FPGA at 175MHz: 42 times faster than software at 3.2GHz

4. 3 Handsfree operation in a noisy environment Speech echoes will appear Disturbing noise, such as:  Engine and fan noise  Wind and tire friction

5. 4 Speech extraction with microphone arrays (beamforming) Microphone arrays allows us to use spatial selectivity (  directional hearing) Microphones separated in space can suppress unwanted locations while passing signals from wanted locations

6. 5 Narrowband beamforming principle Spatial aliasing occurs if the distance between microphones exceeds half signal wavelength

7. 6 Drawbacks with narrowband beamformers Disturbing noise directions are not especially taken into consideration Microphone placement must be accurate (or carefully calibrated) Temporal information is not taken into consideration

8. 7 Broadband beamformer structure FIR-filter Angle of hearing Microphones Output By choosing appropriate FIR filters the angle of hearing can be adjusted for each frequency

9. 8 Adaptive beamforming In order to track variations in the noise situation and to avoid expensive calibration procedures an adaptive scheme is used It consists of two phases  Calibration mode  Operation mode

10. 9 Phase 1: Calibration mode – Select desired location Speaker says a short sentence in a silent car Microphones Estimate covariance matrices Determine coefficients for each filter

11. 10 Phase 2: Operation mode – Speech extraction while driving Microphones Adapting Beamformer Output Speech

12. 11 1. FPGA-based beamformer Low non-recurring engineering cost Architecture exploration Relatively easy to adapt new algorithms Adjust filter length in different environment Embedded processor to produce a system- on-a-chip (SoC) solution  software speech recogniser

13. 12 Comparison between subband and time-domain implementations (#channels I= 6)

14. 13 Subband beamformer structure Angle of hearing

15. 14 FPGA-based architecture

16. 15 DMA interface

17. 16 2. Bitwidth optimisation Software implementation is based on MATLAB Floating point implementation is expensive on FPGA Fixed point implementation  Integer width: avoid overflow  Saturation arithmetic: overflow protection  Fraction width: sufficient accuracy relative to floating point

18. 17 Optimisation results Input signal Noise signal Double precision floating point outputOptimised fixed point output

19. 18 3. Implementation results Target FPGA: Xilinx Virtex 4 SX55 Slice used: 5937 (12%) DSP48 used: 72 (14%) Block RAM used: 8 (2%) Operating frequency: 175MHz Processing rate: 43k sample per second Pentium 4 3.2GHz processing rate: 7.2k sps 6 times faster for 1 instance

20. 19 Multiple instances Scalable by embedding more beamformer instances Useful in the case where more than one signal sources in a system 7 instances can achieve 42 times speed up while running at 175MHz InstancesFrequency (MHz)SlicesDSP48Speed up 117512%14%6.0 317536%42%17.9 517560%70%29.8 717584%98%41.7

21. 20 Future work Implement partial reconfiguration on the filter length based on the change of the environment. Embed a speech recogniser as a performance indicator to adjust the filter length. Optimise the power consumption by reducing the number of glitches in the system.

22. 21 Conclusion Two different beamforming algorithms have represented Narrowband and broadband Subband processing FPGA architecture Processing element DMA interface Bitwidth optimisation 7 instances of beamformer is 42 time faster