This paper proposes a vessel recognition and classification system based on vessel acoustic signatures. Teager Energy Operator (TEO) based Mel Frequency Cepstral Coefficients (MFCC) are used for the first time in Underwater Acoustic Signal Recognition (UASR) to identify platforms the acoustic noise they generate. TEO based MFCC (TEO-MFCC), being more robust in noisy conditions than conventional MFCC, provides a better estimation platform energy. Conventionally, acoustic noise is recognized by sonar oper-ators who listen to audio signals received by ship sonars. The aim of this work is to replace this conventional human-based recognition system with a TEO-MFCC features-based classification system. TEO is applied to short-time Fourier transform (STFT) of acoustic signal frames and Mel-scale filter bank is used to obtain Mel Teager-energy spectrum. The feature vector is constructed by discrete cosine transform (DCT) of logarithmic Mel Teager-energy spectrum. Obtained spectrum is transformed into cepstral coefficients that are labeled as TEO-MFCC. This analysis and implementation are carried out with datasets of 24 different noise recordings that belong to 10 separate classes of vessels. These datasets are partially provided by National Park Service (NPS). Artificial Neural Networks (ANN) are used as a classification method. Experimental results demonstrate that TEO-MFCC achieves 99.5% accuracy in classification of vessel noises. © 2016 IEEE

Akbas C.E.

Can G.

Cetin A.E.

Bilkent University Institutional Repository

RECOGNITION OF VESSEL ACOUSTIC SIGNATURES USING NON-LINEAR TEAGER ENERGY BASEDFEATURESGökmen Can, Cem Emre Akbaş, A. Enis ÇetinDepartment of Electrical and Electronic EngineeringBilkent University, 06800 Bilkent, Ankara, Turkeygokmen@ee.bilkent.edu.tr, akbas@ee.bilkent.edu.tr, cetin@bilkent.edu.trABSTRACTThis paper proposes a vessel recognition and classification sys-tem based on vessel acoustic signatures. Teager Energy Operator(TEO) based Mel Frequency Cepstral Coefficients (MFCC) areused for the first time in Underwater Acoustic Signal Recognition(UASR) to identify platforms the acoustic noise they generate. TEObased MFCC (TEO-MFCC), being more robust in noisy conditionsthan conventional MFCC, provides a better estimation platformenergy. Conventionally, acoustic noise is recognized by sonar oper-ators who listen to audio signals received by ship sonars. The aimof this work is to replace this conventional human-based recognitionsystem with a TEO-MFCC features-based classification system.TEO is applied to short-time Fourier transform (STFT) of acousticsignal frames and Mel-scale filterbank is used to obtain Mel Teager-energy spectrum. The feature vector is constructed by discretecosine transform (DCT) of logarithmic Mel Teager-energy spec-trum. Obtained spectrum is transformed into cepstral coefficientsthat are labeled as TEO-MFCC. This analysis and implementationare carried out with datasets of 24 different noise recordings thatbelong to 10 separate classes of vessels. These datasets are par-tially provided by National Park Service (NPS). Artificial NeuralNetworks (ANN) are used as a classification method. Experimentalresults demonstrate that TEO-MFCC achieves 99.5% accuracy inclassification of vessel noises.Index Terms— MFCC, Teager energy, Vessel Recognition.1. INTRODUCTIONDetection, classification and tracking of vessels are very significantfor war strategies and improving coast and off-shore security. Pas-sive underwater acoustic sensors are used to detect vessels noise andgather data. Vessel noise is an acoustic signature and it can be usedfor vessel detection and classification.Underwater acoustic signal detection methods are based onsounds produced by moving vessels. Passive acoustic methods havebeen used in submarines for years, and these works were publishedafter World War II. The main sources of vessel noise are propellersand engine mechanisms (blades, rotation and propeller radiated pres-sure). There is also environmental noise such as the noise generatedby waves, wind, rainfall, etc. [1].Every person has a unique voice and the characteristic of thisvoice is used for speaker identification in speech recognition. Asmentioned previously, every ship also has a different noise source978-1-5090-5524-1/16/$31.00 c©2016 IEEEcombination (number of blades, rotational speed and propeller radi-ated pressure etc.).This combination creates a unique noise for everyvessel. This uniqueness can be considered as an “acoustic signature”for vessels. As a result, automatic speaker recognition (ASR) andclassification algorithms in speech recognition can be used for Un-derwater Acoustic Signal Recognition (UASR) to identify platformsusing their acoustic signatures.Conventionally, classification of vessel signatures is carriedout by an experienced submarine sonar operator who listens to theacoustic noise coming from the ship sonars. It is evident that itwould be more feasible to develop a computer based vessel signa-ture recognition system using ASR methods. MFCC algorithm iswidely used in speech recognition systems and it produces goodresults on speech recognition and identification. This work offers atechnique that extracts feature vectors using MFCC frame by framefor classification of acoustic vessel signatures. Our work suggeststhat MFCC can easily be adapted to classification of vessel noise athigh classification accuracies [2].This work offers a modified version of MFCC that utilizes TEOfor classification of vessel signatures. Applying non-linear Teager-energy operator can suppress environmental noise and increase theclassification performance [3, 4]. TEO is used in various speechrecognition applications and it is a good candidate for proposed fea-ture parameters in vessel acoustic signatures. The proposed featuresare evaluated to show that TEO-MFCC is more robust than MFCCand it produces higher classification success rate. Neural Network(NN) classifier is used for feature matching.This paper is organized as follows: In section 2, the conventionalMFCC and TEO are presented. In section 3, TEO based MFCC foracoustic vessel noise recognition algorithm is proposed. In section4, the performance of the proposed features is evaluated. Conclusionis presented in section 5.2. THEORETICAL BACKGROUND2.1. Mel Frequency Cepstral Coefficients (MFCC)The first step of automatic speech recognition (ASR) is extractingfeatures in order to identify the linguistic content of the signal anddiscard noise and other carried irrelevant information. Sound is gen-erated in humans by vocal cords and the shape of the vocal tract(tongue, teeth etc.) filters this sound. This shape or filtering deter-mines what sound comes out. If shape is determined accurately, anaccurate representation of the produced phoneme can be obtained.This shape appears in the envelope of the power spectrum of the gen-erated sound. Since the shape of the vocal tract can be representedby MFCC accurately, this method is widely used in ASR [3].The block diagram of the MFCC algorithm is given in Figure 1.Each block is explained in the following subsections.Fig. 1. Block Diagram of MFCC2.1.1. Pre-emphasis Block:This step is the filtering of the vessel acoustic noise by the filter de-fined in Eq.1. This filter boosts higher frequencies so that the energyof the signal increases at higher frequencies after filtering [4]. Thenoise signal has smaller amplitudes at high frequency componentswith respect to low frequencies. After this block, high frequencycomponents of the signal become more significant and more infor-mation about the acoustic model is obtained [5].y[n] = x[n]− 0.95x[n− 1] (1)2.1.2. Framing Block:The importance of this block is that much shorter frames have unreli-able spectral estimate and longer frames change too much. Althoughspeech signal is non-stationary, short time scales will be stationary.The reason for this is that the width of the frames is chosen as 30mswith 20ms overlap. In this case frames are 10ms shifted.2.1.3. Windowing Block:Windowing function smooths the attenuation at both ends of the sig-nal. Ends reduce toward zero and unwanted artifacts can be avoided.In our work, Hamming window is used for this purpose since itsspectrum falls off rather quickly and the resulting frequency resolu-tion is better [6].w[n] ={0.54− 0.46cos( 2πnN−1 ) 0 ≤ n ≤ N − 10, otherwise(2)2.1.4. Mel-Filter Bank Block:Sensitivity of human hearing system is not the same in all frequencybands. Mel (melody) is a unit of measure based on human ear’s per-ceived frequency. Human ears are less sensitive at higher frequen-cies, and more sensitive to small changes in low frequencies thanhigh frequencies. This scaling makes our features closer to whathumans hear. 1 kHz is defined as 1000 Mels as reference [7].Converting formula from linear-scale to Mel-scale in frequencydomain:Mel(f) = 1125× ln(1 + f700) (3)Below 1 kHz, Mel-Scaling is similar to linear frequency spacing.Above 1 kHz, it is similar to logarithmic spacing. Triangular filtersare uniformly spaced in the Mel-Scale [8].Fig. 2. Triangular Mel-Scale filter bankAfter calculating Mel-Scale filter bank in frequency domain, ap-ply them to the spectrum of the signal. The figure below summarizesthis part:Fig. 3. Mel-Filter Bank ProcessingLast step of the Mel-Filter bank block is to calculate the energiesof the filter bank. Multiplying each filter bank with the magnitudespectrum of the signal and then adding up the coefficients yields howmuch energy there is in each filter bank. In previous figures, “Y” isthe energy of the filter bank and “M” is the indicator of which filterbank it is.2.1.5. Logarithm Block:The human perception of sound intensity approximates the logarith-mic scale rather than linear. This means that, humans are less sensi-tive to small changes at high amplitudes than low amplitudes.2.1.6. Discrete Cosine Transform Block (DCT):This block converts logarithmic Mel-spectrum into time domain andthe result is labeled as MFCC. Logarithmic magnitude spectrum isreal and symmetric, so inverse DFT reduces to DCT.y[k] =M∑m=1log(|Y (m)|)cos(k(m− 0.5) πM) k = 0, 1, ...M − 1(4)2.2. Teager Energy Operator (TEO)TEO is an energy operator and it can eliminate the effect of noise infeature extraction. It provides a good estimation of the “real” sourceof energy and ensures that the system will be more robust in noisyenvironments. For clean conditions, the system performs similarly[9].Teager Energy Operator is defined for real continuous-time sig-nals in the following equation:Ψ(x(t)) = ẋ(t)2 − x(t)ẍ(t) (5)and for real discrete-time signals can be written as follows:Ψ(x[n]) = x[n]2 − x[n− 1]x[n+ 1] (6)For the case of complex continuous-time signals, the equation isgiven as:Φ(x(t)) = Ψ(Real{x(t)}) + Ψ(Im{x(t)}) (7)and for discrete time:Φ(x[n]) = Ψ(Real{x[n]}) + Ψ(Im{x[n]}) (8)3. PROPOSED TEO-MFCC FEATURE PARAMETERSApplying non-linear Teager-energy operator (TEO-MFCC) requiresthe following process:Fig. 4. Block Diagram of TEO-MFCCBlue blocks in TEO-MFCC flow diagram is the same withMFCC computation. TEO (red block) is applied to the output ofthe FFT block for each frame in frequency domain. The TEO ofX[k], Φ(X[k]) is calculated and the magnitude ofΦ(X[k]) is thenweighted by Mel-filter bank processing explained in Figure-3. Cal-culation of the energy of each filter bank and other following stepsare the same as MFCC computation. Output of the block diagram infigure-4 is labeled as TEO-MFCC [10].4. EXPERIMENTS AND RESULTS4.1. Experimental SetupThe data sets containing the records of 19 acoustic signatures from6 types of vessels are used in the experiments. The acoustic signa-tures are recorded by an acoustic sensor submerged underwater froma stationary vessel while another vessel moves and produce noise(its acoustic signature). The moving vessel approaches and movesaway from the stationary sensor at different velocities and recordsare taken at varying distances. The distance between the moving andstationary vessels is measured both by GPS and laser range-finderand this distance is synchronized with the acoustic recordings.Fig. 5. Map of experiment and position of vesselsOn the stationary vessel, Reson TC4032 hydrophone is used asthe acoustic sensor. Data acquisition is performed at 100 kHz or 200kHz sampling rate. Records are decimated by a factor of 5 or 10 toprovide 20 kHz sampling rate. As shown in Figure 6, acoustic noiseof a cruise ship decreases significantly after 9 kHz, hence 20 kHzis a sufficient sampling rate for this application. Records are alsodivided into smaller frames in order to be treated as a short record ofthe underwater acoustic signatures.Fig. 6. Power Spectral Density of Acoustic Noise of a Cruise ShipIn addition, records in NPS dataset are used in the experimentsin order to get more realistic experimental results [11]. However,distance between sensor and vessel and velocity are generally un-known in this dataset. Different classes of vessels are chosen (ferry,freighter, cruise ship, and outboard) with respect to previous vesselsto increase database.4.2. ResultsThe data set contains 24 noise records coming from 10 differenttypes of vessels and duration of each record frame in the databaseis 3 seconds long. Our proposed method is tested with various ve-locities of vessels between 5 knots and 26 knots. Table-1 showsacoustic noise of platforms and their velocities.Following parameters are used to extract the feature vectors ofour MFCC and TEO-MFCC methods: Frame size is 25ms with anoverlap of 10 ms, pre-emphasis coefficient is 0.97, number of filter-bank and Cepstral Coefficients varies between 10 and 22 to analyzethe effect of these parameters and the lower and upper frequenciesof the filter-bank are 40 Hz and 4 kHz.Table 1. Vessel Database and DescriptionTypeof Vessel DescriptionVelocity(knot)#of RoundTripTotal #of frameType-A Tug-Vessel 5, 7.5, 10 2 1788Type-B Tug-Vessel 5, 10 2 1192Type-C Tug-Vessel 6, 8, 8.5 1 894Type-DMilitaryVessel 5, 13 1 596Type-EMilitaryVessel 20, 26 1 596Type-FMilitaryVessel 20, 26 1 596Type-GOutboard60hp 10, 20 1 596Type-H Freighter - 1 298Type-ICruiseShip - 1 298Type-J State-Ferry - 1 298A vessel at various velocities is the same target, so we need toshow that MFCC values and distribution are similar regardless ofthe speed of the vessel. However, different types of vessels at thesame velocity are different targets, so MFCC values and distributionshould be different. This is a significant point because MFCC val-ues are feature vectors and inputs for classification. Figures 7, 8 and9 show MFCC distribution and filter-bank energies, when CepstralCoefficients (C) and number of filter-bank (M) are equal to 20. Al-though type-B 5 knots and 10 knots are similar, type-B and type-A5 knots have different distributions.Fig. 7. Type-A 5knot filterbank energy and MFCCClassification of feature vectors problem is solved by NeuralPattern Recognition Application in Matlab Neural Network toolbox[12]. Application classifies features into a set of target categoriesusing a two layer feed-forward network. Network structure has onehidden layer and one output layer. The length of input is equal tothe length of the feature vector, the number of output layer neuronsis equal to the number of classes and the number of hidden layerFig. 8. Type-B 5knot filterbank energy and MFCCFig. 9. Type-B 10knot filterbank energy and MFCCneurons is chosen as 10. Changing the number of hidden layer neu-rons does not change the overall classification accuracy. Thus, thenumber of hidden layer neurons is not a significant parameter in thisapplication. These layers are trained to classify test data according totarget classes. This application is supervised learning, so it updatesthe weights according to the scaled conjugate gradient backpropaga-tion method. Randomly chosen; 70% samples of input data is usedfor training, 15% is used for validation and 15% is used for testing.Robustness and performance of the MFCC and TEO-MFCC fea-ture parameters are compared with various parameters. It is shownthat; when the numbers of MFCC and TEO-MFCC are equal orgreater than the number of the filter bank, classification success rateincreases rapidly and it is stable after that point with a high suc-cess rate. This information should be considered when choosing thenumber of cepstral coefficients.TEO-MFCC performs better than MFCC and TEO is more ef-fective when the number of filter-banks increases. Success rates aregiven in the following figures with various parameters. The maxi-mum recognition accuracy is 99.5% with the implementation of Tea-ger energy operator.Fig. 10. Performance of methods according to # of filterbankFig. 11. Performance of methods according to # of Cepstral Coeffi-cients5. CONCLUSIONSIn this paper, a vessel acoustic signature classification algorithmis proposed. This algorithm uses TEO based MFCC features forclassification. MFCC method is widely used in speech recogni-tion, and TEO increases the performance of classification. MFCCand TEO-MFCC are extracted as feature vectors and their per-formances are compared to the conventional MFCC. TEO-MFCCbased proposed algorithm is a new method in UASR and it is shownthat TEO-MFCC is more robust in noisy conditions than MFCCsince non-linear Teager-energy operator suppresses noise. NN isused for classification and experimental results show that the ves-sel recognition system achieves 99.5% classification accuracy withTEO-MFCC features.The acoustic data of the vessels are recorded in clean conditions.Our major future work is to collect acoustic data of more than onevessel at the same time using hydrophone arrays instead of a singlehydrophone and classify different types of vessels at the same timeusing this beam-formed data. Performance of our proposed methodsare tested in these conditions.As minor future work, different learning techniques and plat-forms will be used to increase recognition accuracy and develop ourproposed method.References[1] K. W. Chung, A. Sutin, A. Sedunov, and M. Bruno, “Demonacoustic ship signature measurements in an urban harbor,” Ad-vances in Acoustics and Vibration, vol. 2011, 2011.[2] T. Lim, K. Bae, C. Hwang, and H. Lee, “Classification of un-derwater transient signals using mfcc feature vector,” in SignalProcessing and Its Applications, 2007. ISSPA 2007. 9th Inter-national Symposium on. IEEE, 2007, pp. 1–4.[3] “Mel frequency cepstral coefficients (mfcc) tutorial,”http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/,Accessed: 2016-06-04.[4] R. Vergin, D. O’Shaughnessy, and V. Gupta, “Compensatedmel frequency cepstrum coefficients,” in Acoustics, Speech,and Signal Processing, 1996. ICASSP-96. Conference Pro-ceedings., 1996 IEEE International Conference on, May 1996,vol. 1, pp. 323–326 vol. 1.[5] F. Karabiber, “Feature extraction-mel frequency cepstral coef-ficients,” Yıldız Technical University Department of ComputerEngineering Lecture Notes, Accessed: 2016-05-29.[6] V. Divyateja, P. A. Kumari, and D. K. Sethi, “Performanceevaluation of feature extraction algorithms used for speaker de-tection,” European Journal of Advances in Engineering andTechnology, vol. 3, no. 1, pp. 34–39, 2016.[7] A. Das, M. R. Jena, and K. K. Barik, “Mel-frequency cepstralcoefficient (mfcc) - a novel method for speaker recognition,”Digital Technologies, vol. 1, no. 1, pp. 1–3, 2014.[8] K. S. Rao and S. G. Koolagudi, Robust Emotion Recogni-tion using Spectral and Prosodic Features, Springer Science& Business Media, 2013.[9] D. Dimitriadis, P. Maragos, and A. Potamianos, “Auditoryteager energy cepstrum coefficients for robust speech recog-nition.,” in INTERSPEECH, 2005, pp. 3013–3016.[10] A. Georgogiannis and V. Digalakis, “Speech emotion recog-nition using non-linear teager energy based features in noisyenvironments,” in Signal Processing Conference (EUSIPCO),2012 Proceedings of the 20th European. IEEE, 2012, pp.2045–2049.[11] “National park service underwater sounds dataset,”https://www.nps.gov/glba/learn/nature/soundclips.htm, Ac-cessed: 2016-06-04.[12] H. Demuth, M. Beale, and M. Hagan, “Neural networktoolboxTM 6,” User’s guide, 2008.

Recognition of vessel acoustic signatures using non-linear teager energy based features

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Recognition of vessel acoustic signatures using non-linear teager energy based features

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