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SU ALTI AKUSTİK SİNYAL TANIMA YÖNTEMLERİ

UNDERWATER ACOUSTIC SIGNAL RECOGNITION METHODS

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Abstract (2. Language): 
The term Underwater Acoustic Signal Recognition (UASR) is used for identifying the platforms by some techniques from the acoustic sound signals they produce. In this paper, we propose two different schemes for UASR. In both schemes, the feature extraction is performed using Mel-Frequency Cepstral Coefficients and Linear Predictive Coding derived Cepstral Coefficients which have been extensively utilized in speech recognition. In the first scheme, the features extracted frame by frame are used as a sequence in the representation of the whole signal. The classification of that sequence of vectors is then performed by Hidden Markov Models with various topologies. The second scheme represents the frame features using Bag of Acoustic Words approach. In training stage, all the feature vectors extracted from the input signal are first clustered into a set of acoustic words. Each feature vector is then assigned to an acoustic word. After the frequency of each word is calculated in the input signal, the final representation is performed by the co-occurrence list of the acoustic words.
Abstract (Original Language): 
Su altı Akustik Sinyal Tanıma (SAST) terimi, platformları ürettikleri seslerden bazı teknikler kullanarak tanıma işlemi için kullanılmaktadır. Her gemi makine, pervane, tekne yapısı ve mürettebat alışkanlıklarının birleşiminden meydana gelen kendine özgü özelliğe sahiptir. Bu makalede, SAST için iki değişik yöntem önermekteyiz. Her iki yöntemde özellik çıkarımı işlemi konuşma tanıma konusunda yarar sağladığı kanıtlanmış Mel-Frekans Kepstral Katsayıları ve Doğrusal Kestirimci Kodlama ile türetilmiş Kepstral Katsayılar ile hesaplanmaktadır. İlk yöntem de öznitelik çıkarımından sonra sinyal vektör dizisi olarak ifade edilir. Vektör dizilerinin sınıflandırılması daha sonra değişik topolojilere sahip Saklı Markov Modelleri ile yapılmaktadır. İkinci yöntem çerçeve özelliklerini Akustik ses kümesi yaklaşımını kullanarak temsil eder. Eğitme safhasında, giriş sinyalinin çerçevelerinden çıkarılan tüm öznitelik vektörleri önce bir akustik kelimeler kümesine gruplandırılır. Öznitelik vektörlerinin her biri bir akustik kelimeye atanmaktadır.

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