Automatic Equalization for Live Venue Sound Systems Damien Dooley, Final Year ECE Progress To Date, Monday 21 st January 2008

Contents Background Progress to Date Future Plans

Background In live venues it can be quite challenging to get a balanced sound Several factors can influence a significant deviation between the desired output and the actual output of the system. Factors include, room dimensions, room structure, number of people in the room, furniture, sound source and listener location.

The system will consist of a microphone which will record the sound at a particular location in the room. A pre-determined sound sequence will be played through the PA and the DSP system will record the audio signal received. The system will then estimate the impulse response of the system which will be a combination of the input signal and the room acoustics.

A test sequence is played in order to calculate the impulse response Both clean and distorted signals fed into DSP element DSP Calculates deviation between clean and distorted signals Inverse Fourier transform created to destructively interfere with predicted noise signals DSP O/P = (Clean Signal – Noisy Signal) -1

Room Acoustic Modeling In order to properly understand how to implement this design, one must understand how sound behaves in various rooms. This is known as room acoustic modeling. When a sound is generated in a room the listener will first hear the sound via the direct path to the source. The listener will then hear the reflections in the sound and the magnitude will decrease exponentially after each echo.

MATLAB Room Model The following code was used to simulate the room response to the audio [d,fs]=wavread('ReverbVoice.wav'); %Takes in Voice Sample 'd'-->Sampled Data | 'fs'--> Sampling Frequency num=[0.8,zeros(1,3000),1];%-->Numerator array for filter, note initial coefficient 0.8. den=[1,zeros(1,3000),0.8];%-->Denominator array, this gives the samples an oscillating decay factor of 0.8 %In both cases the "zeros" elements in the array provides the delay between repeated sounds, in this case 3000 samples of a delay with %a sampling frequency of 44100Hz, gives a sampling delay of 70 ms approx d1=filter(num,den,d);%Applying the filter coefficients to the data wavwrite(d1,fs,'FilteredVoice.wav');%Write the output to a new sound file

Impulse Response

 Before After  Exponential Falloff

Future Plans Research into various FFT algorithms is being carried out at the moment into how best to apply the “anti-sound” An adaptive filter, least mean squared will be used to select the most appropriate model based on the impulse response information gathered from the test sequence played. This will eventually be implemented on a DSP board. The signals will then be processed in real-time and the automatic graphic equalizer should then be completed.

Questions?