Study of Directivity and Sensitivity Of A Clap Only On-Off Switch

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1 Study of Directivity and Sensitivity Of A Clap Only On-Off Switch Ajaykumar Maurya Dept. Of Electrical Engineering IIT Bombay Sarath M Dept. Of Electrical Engineering IIT Bombay Abstract Clap clap switches find applications in domains such as automation and security. A clap sound can be used as a triggering signal either to switch the relays on/off, or as alert signal for some Security System. However the sound is very directive and sensitive to noise floor. Also,being an impulse,it is difficult to distinguish between a clap and a similar impulsive sounds like bang,dog-barking, coughing,etc. Our report presents an efficient way to design omni-directional device for detecting clap from a distance and be able to distinguish it will other impulsive sounds using time and frequency domain analysis. Keywords Impulse,Correlation,Sampling,Threshold,Directivity, Sensitivity I. INTRODUCTION The basic motivation of the project was to develop a product which will make a person s life simpler, say to turn ON a particular device or an appliance, rather than the customary practice of switching ON the device explicitly, a simple clap would suffice, thereby reducing the human effort. In case of emergency, a clap switch can be used an emergency alert device. A. Block Diagram Clapping generates sound waves. A sensors which converts these sound waves into electrical signal is desired to detect the occurrence of sound event. Hence a microphone is used to sense audio signal whose output voltage level typically is of the order of few millivolts depending upon the loudness of sound and distance of source from the microphone. The block diagram of the circuit designed for the project is shown in Figure 1. Fig. 1: Simplified Block Diagram It consists of an op-amp based amplifier circuit, sufficient enough to amplify the electrical signals generated by microphone. This amplified signal is then fed to the Tiva TM4C129 module, wherein we perform some signal processing to distinguish clap signals from other randomly occurring signals. B. Basic Design Elements The clap activated switching device can basically be described as a low frequency sound pulse activated switch that is free from false triggering. The input component is a transducer that receives clap sound as input and converts it to electrical pulse.the transducer (microphone) is connected to an amplifier sub-circuit which is then fed to Tiva TM4C module. 1) The Sensor(Microphone): Microphones converts acoustic energy i.e. sound signal into an electrical signals. Basically, a microphone is made up of a diaphragm, which is a thin piece of material that vibrates when it is struck by sound wave. This causes other components in the microphone to vibrate leading to variations in some electrical quantities thereby causing electrical current to be generated. The current generated in the microphone is the electrical pulse. There are two major types of microphones based on the technical methods of converting sound into electricity namely the dynamic and condenser microphone. Condenser microphones generally have flatter frequency responses than dynamic, and therefore mean that a condenser microphone is more desirable if accurate sound is a prime consideration as required in this design. And hence, we have also preferred the same in the form of electret microphone. Figure 2. shows the basic circuit diagram for an electret microphone. An electret is a thin, Teflon-like material with a fixed charge bonded to its surface. The electret is housed between two electrodes, and the structure forms a capacitor which contains a fixed charge. Air pressure variations (sound waves) move one of the electrodes of the capacitor back and forth, changing the distance between the two electrodes, and modulating the capacitance of the structure. Because the charge on the microphone is fixed, varying the capacitance causes the voltage on the capacitor to also change, satisfying the equation: Q = CV (1) where Q is charge, C is capacitance, and V is voltage. Therefore the microphone capacitor acts as an accoupled voltage source. Because the charge on the microphone capacitor must be fixed, the amplifier circuitry directly in contact with it must have extremely high input impedance. Most electret microphones have an internal JFET which buffers the microphone capacitor. The voltage signal produced by sound modulates the gate voltage of the JFET, labeled VG in Figure 2 causing a change in the current flowing between the drain and source of the JFET (IMIC). An extremely high resistance, RG, may be included to bias the gate of the JFET.

2 Fig. 4: Basic Amplifier Circuit Fig. 2: Electret Microphone Circuit Fig. 3: Clap Waveform At figure 2 output flash memory. Since our idea was to perform certain signal processing on the samples of the external signals and distinguish it from the desired clap signal, both time domain and frequency domain analysis had to be performed. Tiva TM4C module has 12 bit precision ADC with a maximum sampling rate of 1 million samples per second. Also these, samples so generated can be directly processed in frequency domain using fast fourier transform. Thereby making the processing much simpler. II. DESIGN FLOW The electrical signals so generated from microphone are only of the order of millivolts, and hence an amplification stage is required. A non-inverting amplification stage using op-amp was hence designed initially with a variable gain and then the gain was fixed at 100 after observing the output wave forms. This stage was followed by a comparator circuit, which basically compares the amplified signal level with a predefined threshold which is depicted in figure 3. 2) Amplifier Stage: Operational amplifiers can be used in two basic configurations to create amplifier circuits. One is the inverting amplifier where the output is the inverse or 180 out of phase with the input, and the other is the non-inverting amplifier where the output is in the same sense or in phase with the input. The gain of the non-inverting amplifier circuit can be determined by the using the fact that the voltage at both inputs is the same. This arises from the fact that the gain of the amplifier is exceedingly high. If the output of the circuit remains within the supply rails of the amplifier, then the output voltage divided by the gain means that there is virtually no difference between the two inputs.and hence the gain is given by V out = 1 + R 2 R 1 V in (2) 3) Tiva TM4C Module: One of the prime requirement of our project was the huge amount of samples that we needed to store on the micro-controller, and hence Tiva TM4C was chosen as it has 256 KB of data memory and 1 MB of Fig. 5: Amplifier and Stage However, since our aim was to separate clap signals from almost all other signals, comparator stage is unnecessary and hence we removed the comparator stage. Now, from literature survey, it was found that clap signal had a frequency range ranging approximately from 2kHz to 3kHz, hence we designed a high quality factor band pass filter so as to notch out other undesired frequencies.

3 Fig. 6: A high Q band pass filter For the figure 5. the transfer function is given by, V o scr = V i 1 + s 2 C 2 R 2 + scr(2 K) where, K is given by, And bandwidth is given by, (3) K = R 4 R 5 (4) f H f L = (2 K) (5) However, desired result was not obtained even after the amplified analog signal was filtered using high Q band pass filter as undesired signals like desk banging etc where not getting properly filtered. Hence, we resorted to process the signals on Tiva TM4C board and analyze the samples. III. HARDWARE IMPLEMENTATION The clap-detector circuit was implemented on on a general purpose PCB as shown in figure 7. We have 4 LED s indicating the confidence levels of clap detection. The Tiva TM4C is programmed such that the when the real time samples passes each thresholds both frequency domain as well as time domain, the LED s indicating the confidence levels blinks in sequence. Also another LED has been set up to indicate the detection of signals other than clap signals. IV. SIGNAL PROCESSING The signals from different possible sources are sampled using ADC in Tiva TM4C with appropriate sampling rate. The samples were analyzed in both time domain and frequency domain.in time-domain the captured samples were crosscorrelated with a pre-loaded training sequence. Initial training is necessary as claps can be different for different person. In frequency domain Fast Fourier Transform(FFT) was performed on the samples so as the distinguish different frequencies. Appropriate results were displayed to distinguish the clap from other sounds. A. Time Domain Analysis Fig. 7: Clap Detector-Hardware The continuous time analog time domain signal from the amplifier circuit was fed to the analog read pin of Tiva TM4C module. The clap signals correspond to a frequency range of 2kHz to 3kHz, and hence the signals were sampled at a rate of 10k samples per second, satisfying the Nyquist criteria. Initially training sequences were generated by repeatedly clapping predefined number of times. The training sequence so obtained was recorded and analyzed for its time domain specification. These specifications along with each signals is pre-loaded as a training data in the Tiva module. For our application we have chosen the training data to be 10. Once the training data is loaded, the real time testing data is fed to the analog pin of Tiva. The sample rate (approx. 10K samples) and the number of samples (128) is kept same so as to correlate the data without zero padding.time domain correlation is done using Pearson s formula. numerator = X1 X2 X1 X2 (6) denominator1 = X1 X1 X1 X1 (7) denominator2 = X2 X2 X2 X2 (8) numerator r = (9) denominator1 denominator2 where X1 and X2 are the sample Vector to be correlated. The maximum output of ADC of Tiva TM4C is Also the maximum number to which a float is defined in Tiva is 4e9. By using Pearson s formula the summation term was crossing the maximum limit of a float thereby giving false reading for the same. Thus an optimal number of points was required to choose so as to get time time correlation as well as frequency resolution in frequency domain as per required. Hence 128 sample points were chosen where in the maximum limit the summation term can reach is 2.2e9 and the frequency resolution is 144Hz which is acceptable for our application.

4 B. Frequency Domain Analysis The sampled real time signal is processed using 128 point DFT in frequency domain to detect the presence of frequencies between 2khz to 3 khz. The resolution for 128 point DFT is about 144 Hz. Also the amplitude for the corresponding frequencies plays a very important role as high amplitude may lead to false triggering. Higher point DFT can also be done as it will increase the resolution but on the cost of process time. We went for for 128 point DFT as higher points couldn t be processed on Tiva TM4C because of range limitation of floating point numbers. V. ALGORITHM Initially, the clap signals are amplified by a non-inverting amplifier of sufficient gain. This amplified signal is fed to the Tiva TM4C. Tiva is programmed to sample the analog input at the rate of approx. 10k samples per second. These samples so obtained are analyses in frequency-domain as well as timedomain. Initial analysis is done in frequency domain, were we filter out the incoming signals which do not fall in the range of clap-signals. This is done by specifying appropriate thresholds for the amplitudes in the frequency domain during the training process. Once we have filtered out all the signals which are out of band of the clap frequencies, we switch to time-domain analysis. In time domain we correlate the real time samples with the pre-loaded training sequence. After computing the correlation co-efficient, appropriate threshold is specified on the value of correlation coefficient so obtained during the training process. If the value of correlation coefficient is greater than the threshold then we say that a clap is detected. Figure 8 depicts the flow chart of the algorithm that we have implemented. VI. ANALYSIS AND INTERPRETATION Initially the system was trained with five claps at both near and far. By near clap, we mean a clap at an approximate distance of 30cm, and by far clap we mean a clap at a distance of 2-3m. These signals were sampled on Tiva TM4C and analyzed in Matlab. After repeating the experiments a number of times, the thresholds for both time-domain and frequency domain were set. Also these samples were preloaded into the Tiva TM4C for correlating it with the real time signals. Figures 8 to 25 and 12 to 29 depicts the training signals along with their FFT s processed in Matlab after extracting the samples using. As can be seen from the training data, a majority of the fret iva T M 4Cquencies for the clap signals, both near and far clap falls in the frequency range of 2-3kHz.Accordingly we chosen the lower cutoff frequency to be khz and the upper cutoff frequency to be khz. The frequency domain plots were processed with a resolution of about Hz as there was a computational limitation associated with the floating point numbers while loading the program onto the Tiva TM4C. Similarly after analyzing multiple data sets and correlating it with the preloaded training sequence using equation 9, a threshold of 0.15 was set on the value for correlation coefficient for clap detection. Fig. 8: Flow-Chart

5 Fig. 9: Far 1 Waveform Fig. 13: Far 1 Frequency Response Fig. 10: Far 2 Waveform Fig. 14: Far 2 Frequency Response Fig. 11: Far 3 Waveform Fig. 15: Far 3 Frequency Response Fig. 12: Far 4 Waveform Fig. 16: Far 4 Frequency Response

6 Fig. 17: Far 5 Waveform Fig. 21: Far 5 Frequency Response Fig. 18: Near 1 Waveform Fig. 22: Near 1 Frequency Response Fig. 19: Near 2 Waveform Fig. 23: Near 2 Frequency Response Fig. 20: Near 3 Waveform Fig. 24: Near 3 Frequency Response

7 Fig. 25: Near 4 Waveform Fig. 29: Near 4 Frequency Response Fig. 26: Near 5 Waveform Fig. 30: Near 5 Frequency Response Fig. 27: Test 1 Waveform Fig. 31: Test 1 Frequecy Response Fig. 28: Test 2 Waveform Fig. 32: Test 2 Frequency Response

8 Fig. 33: Test 3 Waveform Fig. 37: Test 3 Frequency Response Fig. 34: Test 4 Waveform Fig. 38: Test 4 Frequency Response Fig. 35: Test 5 Waveform Fig. 39: Test 5 Frequency Response Fig. 36: Test 6 Waveform Fig. 40: Test 6 Frequency Response

9 VII. RESULTS Correlation Table Testing Signals Freq(KHz) Near1 Near2 Near3 Near4 Near5 Far1 Far2 Far3 Far4 Far5 Pen-drop Click Bang Whistle Clap Clap The Correlation table shows the correlation co-efficient for various real time test signals with respect to stored sample. The frequency for Pen-drop, click bang and whistle were not in the range hence rejected. For the claps, as the frequency is within the band, time correlation is done with the store sample. For clap1, the correlation with near4 and far4 is greater than the threshold thus it is detected as a clap. Similarly for clap2 near2, near3, far2 and far3 correlation exceeds and get detected as clap. VIII. CONCLUSION AND FUTURE WORK We have successfully designed and implemented an efficient clap only detector. The system is highly sensitive and omni-directional. Testing the system showed 80% accuracy(40 out of 50 claps were detected) for a maximum distance of 4 meters. Out of remaining 20%, 12% were false negative and 8% were false positive. Overall this system is much more efficient compared to other existing counterparts. Future work includes more regressive training of the system using other efficient algorithms. ACKNOWLEDGMENT We would like to thank Prof. Siddharth Tallur for constantly helping and motivating us constantly in completion of the project. And Prof. Joseph John for giving insights on microphone operation. We also like to thank WEL Lab staffs, in particular Maheshwar Mangat Sir for his valuable inputs. REFERENCES [1] Wasim Ahmad and Ahmet M. Kondoz, Analysis And Synthesis Of Hand Clapping Sounds Based On Adaptive Dictionary, in Proceedings of the International Computer Music Conference University of Surrey, Guildford, United Kingdom [2] Seyi Stephen Olokede,Design of a Clap Activated Switch, in Leonardo Journal of Sciences, College of engineering and technology Olabisi onabanjo university,ibogun,ogun State [3] Bruno H. Repp,Sound Of Two Hands Clapping-An Exploratory Study, in The Journal of the Acoustical Society of America, Haskins Laboratories, Connecticut 1986.