%0 Journal Article %1 Averbuch201122 %A Averbuch, Amir %A Zheludev, Valery %A Neittaanmäki, Pekka %A Wartiainen, Pekka %A Huoman, Kari %A Janson, Kim %D 2011 %J Applied Acoustics %K acoustics classification myown wavelet %N 1 %P 22 - 34 %R 10.1016/j.apacoust.2010.09.006 %T Acoustic detection and classification of river boats %U http://www.sciencedirect.com/science/article/B6V1S-5178VTK-1/2/1ada5a89026bcac0331376262f01af58 %V 72 %X We present a robust algorithm to detect the arrival of a boat of a certain type when other background noises are present. It is done via the analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. We characterize the signals by the distribution of their energies among blocks of wavelet packet coefficients. To derive the acoustic signature of the boat of interest, we use the Best Discriminant Basis method. The decision is made by combining the answers from the Linear Discriminant Analysis (LDA) classifier and from the Classification and Regression Trees (CART) that is also accompanied with an additional unit, called Aisles, that reduces false alarms rate. The proposed algorithm is a generic solution for process control that is based on a learning phase (training) followed by an automatic real time detection while minimizing the false alarms rate.

@article{Averbuch201122, abstract = {We present a robust algorithm to detect the arrival of a boat of a certain type when other background noises are present. It is done via the analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. We characterize the signals by the distribution of their energies among blocks of wavelet packet coefficients. To derive the acoustic signature of the boat of interest, we use the Best Discriminant Basis method. The decision is made by combining the answers from the Linear Discriminant Analysis (LDA) classifier and from the Classification and Regression Trees (CART) that is also accompanied with an additional unit, called Aisles, that reduces false alarms rate. The proposed algorithm is a generic solution for process control that is based on a learning phase (training) followed by an automatic real time detection while minimizing the false alarms rate.}, added-at = {2011-03-08T12:10:31.000+0100}, author = {Averbuch, Amir and Zheludev, Valery and Neittaanmäki, Pekka and Wartiainen, Pekka and Huoman, Kari and Janson, Kim}, biburl = {https://www.bibsonomy.org/bibtex/22f9df40351be9f7be1034e2f42ffb839/pejowart}, description = {ScienceDirect - Applied Acoustics : Acoustic detection and classification of river boats}, doi = {10.1016/j.apacoust.2010.09.006}, groups = {public}, interhash = {ae110e058d6d15517a3ef2981815ac23}, intrahash = {2f9df40351be9f7be1034e2f42ffb839}, issn = {0003-682X}, journal = {Applied Acoustics}, keywords = {acoustics classification myown wavelet}, number = 1, pages = {22 - 34}, timestamp = {2012-06-20T10:05:30.000+0200}, title = {Acoustic detection and classification of river boats}, url = {http://www.sciencedirect.com/science/article/B6V1S-5178VTK-1/2/1ada5a89026bcac0331376262f01af58}, username = {pejowart}, volume = 72, year = 2011 }