1. Automatic Equalization for Live Venue Sound Systems Damien Dooley, Final Year ECE Final Presentation, Thursday 10 th April 2008

2. Presentation Outline Project Background Major milestones and how they were achieved Conclusion

3. Project Background Difficult to get a good sound in enclosed venues Sound Engineer required to balance room acoustics Room acoustic phenomena cause impurities in sound Project aim to develop a system to counteract the negative effects of room acoustics on audio

4. The System Performer needs to convey audio clearly. Room serves to add a degree of nonlinearity to the audio. Microphone picks up room audio and feeds to DSP board together with clean sound directly from the source. DSP board attenuates irregularities to produce a cleaner sound.

5. Room Acoustic Modelling Science of determining how sound propagates though a room from source to observer. Typical impulse response of a room results in a series of exponentially decaying reflections, known as reverberation. Impulse response graph showing room reflections. Time taken to decay can be calculated using Sabine’s equation for reverberation time.

6. MATLAB MATLAB was used to create an accurate model of the room environment. Firstly a delay was created and then a natural sounding reverb as follows. num=[0.8,zeros(1,3000),1]; den=[1,zeros(1,3000),0.8]; d1=filter(num,den,d);

7. Utterance of the word “Reverberation Algorithm” prior to processing. Exponential falloff in Peaks indicate reverberation being applied to data.

8. Next Step Model now created to show reverberation Next step is to create a filter to eliminate such reverberation An adaptive filter is required for such a task

9. Arrangement of the adaptive filter within the system

10. Aside: Simulink Simulink is an extension to MATLAB and uses a more graphical approach to system design

11. Adaptive Filter in MATLAB Initial code contained the LMS adaptive filter as an object ha = adaptfilt.lms(taps,mu); [y,e] = filter(ha, b, a); Step size mu determines rate of convergence of adaptive filter towards ideal coefficients Adaptive filter is a type of filter that adjusts it’s coefficients according to the input

12. Initial Result Original Signal Reverberated Signal Filtered Signal

13. Better Understanding of the LMS r(i) = h(i)*x(i); e(i) = y(i) - r(i); h(i+1) = h(i) + (2*mu*e(i)*x(i)); Sample test data (non-audio) was applied to the following algorithm

14. Step size 2.5 x 10 -6 Step size 5 x 10 -6

15. Real Time Adaptive Filtering

16. Initial Delay Code for(msec = 0; msec < 5000; msec++) { for (sample = 0; sample < 48; sample++) { while(!DSK6416_AIC23_read(hCodec, &data)); audio[sample] = data; delayAudio[(msec+delay)%5000][sample] = audio[sample]; EchoAudio[msec][sample] = audio[sample]+0.5*(delayAudio[msec][sample]); while (!DSK6416_AIC23_write(hCodec, EchoAudio[msec][sample])); } Circular Buffer Usage

17. Real Time LMS Filter Test r[0] = 0 * 100 = 0 b[0] = 2 – 0 = 2 h[1] = 0 + (2 * 0.00001 * 100 * 2) = 0.004

18. Audio Test Output values Converging Coefficients

19. Conclusion Project aim to develop a system to counteract the negative effects of room acoustics on audio. Achieved to a substantial extent, project capable of cancelling delay.

20. Potential Improvements Project can be extended to include reverberation 2 independent signals can be used Algorithm can be tested in a real live environment

21. Thanks!

22. Questions…