Signal Analysis Research (SAR) Group - RNet - Ryerson University

Recommendations

Info

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings. [4] A. Ramalingam and S. Krishnan, “A novel robust image watermarking using a chirp based technique,” In Proc. Canadian Conference on Electrical and Computer Engineering, pp. 1889-1892, May 2004. [5] S. Erkucuk, S. Krishnan, and M. Zeytinoglu, “Robust audio watermarking using a chirp based technique,” Proc. International Conference on Multimedia and Expo, 2003. ICME 03, pp. II - 513-16, July 2003. [6] G.F. Boudreaux-Bartels and T. Parks, “Time-varying filtering and signal estimation using wigner distribution synthesis techniques,” IEEE Signal Processing Magazine,vol. 9, no. 2, pp. 21-67, April 1992. [7] W. Krattenhaler and F. Hlwatsch, “Bilinear signal synthesis,” IEEE Transactions on signal processing, vol. 40, no. 2, pp. 352–363, Feb 1992. [8] W. Krattenhaler and F. Hlawatsch, “General signal synthesis allgorithms for smoothed version of wigner distribution,”Proc. IEEE International Conference on Acoustics, and speech, and Signal Processing (ICASSP), no. 3, pp. 1611–1614, Apr 1990. [9] A. Francos and M. Porat, “Analysis and synthesis of multicomponent signals using positive time-frequency distributions,” IEEE Transactions on Signal Processing, pp. 47(2):493-504, Feb. 1999. [10] S. Peleg and B. Friedlander, “Multicomponent signal analysis using the polynomial-phase transform,” IEEE Transactions on Aerospace and Electronic Systems, vol. 32, no. 1, pp. 378-387, Jan 1996. [11] S. Peleg and B. Friedlander, “The discrete polynomial-phase transform,” IEEE Transactions on Signal Processing,vol. 43, no. 8, pp. 1901-1914, August 1995. [12] M.G. Amin, “Interference mitigation in spread spectrum communication systems using time-frequency distributions,” IEEE Trans. Signal Processing, vol. 45, no. 1, pp. 90–101, Jan 1997. [13] J.D. Laster and J.H. Reed, “Interference rejection in digital wireless communication,” IEEE Signal Processing Mag., pp. 37–62, May 1997. [14] L. Lee, “Time-frequency signal synthesis and its application in multimedia watermark detection,” Master Thesis, Ryerson University, 2005. 19 2984 Authorized licensed use limited to: Ryerson University Library. Downloaded on July 7, 2009 at 11:52 from IEEE Xplore. Restrictions apply.
Emotion Recognition Using Novel Speech Signal Features Talieh Seyed Tabatabaei, Sridhar Krishnan Department of Electrical and Computer Engineering Ryerson University Toronto, Canada {tseyedta, krishnan}@ee.ryerson.ca Abstract—Automatic Emotion Recognition (AER) is a very recent research topic in the Human-Computer Interaction (HCI) field which still has much room to grow. In this contribution a set of novel acoustic features and Least Square-Support Vector Machines (LS-SVMs) are proposed to set up a speaker- independent Automatic Human Emotion Recognition system. Six discrete emotional states are classified throughout this work: happiness, sadness, anger, surprise, fear, and disgust. Different multi-class SVM methods are implemented in order to get the best result. The result achieved by LS-SVM is then compared by that of a Linear Classifier. We achieved an overall accuracy of 81.3%. I. INTRODUCTION Research has been done on emotion in the fields of psychology and physiology for a long time. More recently it’s been the subject of interest to engineers. Its most important application is in intelligent human-machine interaction. As computers have become an integral part of our lives, the need for a more natural communication interface between humans and machines has arisen. To accomplish this goal, a computer would have to be able to perceive its present situation and respond in a different manner depending on its perception. To make Human-Computer Interaction (HCI) more natural and friendly, it would be beneficial to give computers the ability to recognize situations the same way a human does. In today’s HCI systems, machines can recognize the speaker and also content of the speech, using speech recognition and speaker identification techniques. If machines are equipped with emotion recognition techniques, they can also know “how it is said” to react more appropriately, and make the interaction more natural. Other potential applications of Automatic Emotion Recognition (AER) include psychiatric diagnosis, intelligent toys, lie detection, learning environment, customer service, educational software, and detection of the emotional state in telephone call center conversations to provide feedback to an operator or a supervisor for monitoring purposes. One of the most important human communication channels is auditory channel which carries speech and vocal 1-4244-0921-7/07 $25.00 © 2007 IEEE. 345 Aziz Guergachi Department of Information Technology Management Ryerson University Toronto, Canada a2guerga@ryerson.ca intonation. In fact people can perceive each other’s emotional state by the way they talk. Therefore in this work we are analyzing the speech signal in order to set up an automatic system to recognize the human emotional state. Different researchers have decided on different number and kinds of emotional states, such as 3 categories of positive(joy), negative(anger, irritation), and neutral in [7], 4 categories of neutral, anger, Lombard, and loud in [9], and 5 categories of neutral, happiness sadness, anger, and fear in [8]. In this work we are automatically categorizing six different human emotional states: anger, happiness, fear, surprise, sadness, and disgust. Some researchers have developed speaker-dependent speech emotion recognition systems [7, 12]. We think that speaker independency is one of the intrinsic characteristics of an AER system. When a system is person-dependent the accuracy increases but on the other hand, for each new person we have to train our system all over again and that is a major drawback. So here we are trying to reach a very satisfying accuracy with a person-independent system by choosing right acoustic features and powerful classifier. While some researchers have utilized both acoustic characteristics and textual content of an emotional spoken utterance [10, 12], we are conducting our work using commonly used and newly proposed acoustic features of the speech signal only. Various different classifiers have been taken into consideration for categorizing the emotional states. The most common classifiers used are Hidden Markov Model (HMM) [13] and Neural Networks (NNs) [15], whereas the number of works which use Support Vector Machines (SVMs) are relatively very few [12]. SVM is a relatively new approach in the field of Machine Learning and has a large number of advantages to other conventional and popular classifiers like NNs. In this contribution we are using Least Square Support Vector Machines (LS-SVMs), which are the reformulations to the original SVMs. The paper is organized as follows: Section II explains the emotion database used in this research. Section III demonstrates the structure of the AER system proposed in this Authorized licensed use limited to: Ryerson University Library. Downloaded on July 7, 2009 at 11:29 from IEEE Xplore. Restrictions apply. 20
Page 1 and 2: Signal Analysis Research (SAR) Grou
Page 3 and 4: 2006.05 Support Vector Machines Bas
Page 5 and 6: 2001.05 Instantaneous Mean Frequenc
Page 7 and 8: Class A Class B TWFB Mapping Discri
Page 9 and 10: as an added advantage of this appro
Page 11 and 12: Combining Vocal Source and MFCC Fea
Page 13 and 14: probability distribution for calcul
Page 15 and 16: Proceedings of the 29th Annual Inte
Page 17 and 18: Fig. 1. Typical images of the small
Page 19 and 20: This full text paper was peer revie
Page 21 and 22: Frequency 1 0.9 0.8 0.7 0.6 0.5 0.4
Page 23: Frequency TABLE I RESULTS WITH LINE
Page 27 and 28: points [1, 2, 3] (Fig. 2). SVM is b
Page 29 and 30: A WATERMARKING METHOD FOR SPEECH SI
Page 31 and 32: In order to exploit the masking eff
Page 33 and 34: Chirp-based image watermarking as e
Page 35 and 36: 3.2.2 Discrete Polynomial Phase Tra
Page 37 and 38: 2006 International Joint Conference
Page 39 and 40: Fig 2 Two-dimensional mapping (Left
Page 41 and 42: p3 Feature Extraction u Database +
Page 43 and 44: 1424403677/06/$20.00 ©2006 I
Page 45 and 46: = − = K ≤
Page 47 and 48: DISCRETE POLYNOMIAL TRANSFORM FOR D
Page 49 and 50: 3.2. Watermark detection Fig.2 show
Page 51 and 52: IMPROVING POSITION ESTIMATES FROM A
Page 53 and 54: _ . r W-. n r We tried two ways of
Page 55 and 56: Acknowledgements This work of Ryers
Page 57 and 58: ing two consecutive keys. Another c
Page 59 and 60: Table 1. Experimental Results for F
Page 61 and 62: D as { gγ γ ∈ Γ, g = 1} = γ ,
Page 63 and 64: frequency parameters issued from th
Page 65 and 66: necessity of prediction, controllin
Page 67 and 68: 4. Experimental Results The objecti
Page 73: Proceedings of the 2005 IEEE Engine
Page 78 and 79:
the whole spectrum but on non-overl
Page 80 and 81:
Identification Rate 1 0.9 0.8 0.7 0
Page 82 and 83:
signals. On some occasions, when us
Page 84 and 85:
For our first test, we recorded two
Page 86 and 87:
Frequency bands F4 F3 F2 F1 ME5 s1
Page 88 and 89:
as F1, F2, F3 and F4 as shown in Fi
Page 90 and 91:
Figure. 1. Motion Vector magnitudes
Page 92 and 93:
All the above descriptor were quant
Page 94 and 95:
2. METHODOLOGY 2.1. Local Discrimin
Page 96 and 97:
. each group, the posterior probabi
Page 98 and 99:
A NOVEL ROBUST IMAGE WATERMARKING U
Page 100 and 101:
where n is the distortion component
Page 102 and 103:
A Novel Way of Lossless Compression
Page 104 and 105:
111. IMPLEMENTATION As we have pres
Page 106 and 107:
CONTENT BASED AUDIO CLASSIFICATION
Page 108 and 109:
Entropy 4.5 4 3.5 3 2.5 2 1.5 1 0.5
Page 110 and 111:
MODIFIED LOCAL DISCRIMINANT BASES A
Page 112 and 113:
Tree Decomposition (0,0) (1,0) (1,1
Page 114 and 115:
RADIO OVER MULTIMODE FIBER FOR WIRE
Page 116 and 117:
3. COMPARISON OF MULTIMODE FIBER AN
Page 118 and 119:
SUB-DICTIONARY SELECTION USING LOCA
Page 120 and 121:
0 : SI 92 r3 54 =-ax Tirnep"idfh :
Page 122 and 123:
Proceedings of the 25h Annual Inter
Page 124 and 125:
estimated noise generated as the ou
Page 126 and 127:
ROBUST AUDIO WATERMARKING USING A C
Page 128 and 129:
where 0 is the angle of the ray pat
Page 130 and 131:
TIME-FREQUENCY FILTERING OF INTERFE
Page 132 and 133:
3. INTERFERENCE DETECTION. nouen AN
Page 134 and 135:
A GENERAL PERCEPTUAL TOOL FOR EVALU
Page 136 and 137:
Fig. 2. Snapshot of the GUI used fo
Page 138 and 139:
Non-Stationary Noise Cancellation i
Page 140 and 141:
signal and noise spectra overlap, f
Page 142 and 143:
Fig. 4: Output of the Comb filter.
Page 144 and 145:
Indoor infrared transmission suffer
Page 146 and 147:
The inverse wavelet transform is de
Page 148 and 149:
The simulations has been done using
Page 150 and 151:
Figure 6: The original Gaussian noi
Page 152 and 153:
147 Authorized licensed use limited
Page 154 and 155:
Page 156 and 157:
Page 158 and 159:
Page 160 and 161:
Page 162 and 163:
Page 164 and 165:
Page 166 and 167:
Page 168 and 169:
Page 170 and 171:
Figure 2: Block diagram of the FBB
Page 172 and 173:
Figure 4: Benign mammogram before F
Page 174 and 175:
. Table 1: Compression ratios of be
Page 176 and 177:
Most Cohen’s class TFD derived fr
Page 178 and 179:
the desired frequency marginal m(w)
Page 180 and 181:
constructing an adaptive TFD and ex
Page 182 and 183:
Proceedings of the 22"d Annual EMBS
Page 184 and 185:
are: Proceedings of the 22"d Annual
Page 186 and 187:
may be written as M-1 = (z,grn)gm,
Page 188 and 189:
e 1 os 07- Ohzo5- 04- 09- 02- 01- O
Page 190 and 191:
using the denoised signals. As an i
Page 192 and 193:
signals are: 1) model-based TFD, 2)
Page 194 and 195:
compared to that in the MP dictiona
Page 196 and 197:
fied. In addition to being positive
Page 198 and 199:
08 [3] S. Peleg and B. Friedlander.
Page 200 and 201:
Proceedings - 19th International Co
Page 202 and 203:
Proceedings - 19th International Co
Page 204 and 205:
the x and y axes correspond to the
Page 206 and 207:
Fig. 5. Results with synthetic sign
Page 208 and 209:
the users and is explained in next
Page 210 and 211:
,-t 1 2 J 4 5 e 7 e e 10 SNR (I" de
Page 212 and 213:
'il! (c) (a) Weakea User 2nd Strong
Page 214 and 215:
18th Annual International Conferenc
Page 216 and 217:
quisition board and Lab Windows (Na
Page 218 and 219:
Table 1: Comparison of different cl
Page 220:
K. Umapathy and S. Krishnan, Low bi
show all

Signal Analysis Research (SAR) Group - RNet - Ryerson University

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?