Esfahanian M, Zhuang H, Erdol N
Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, Florida 33431
J Acoust Soc Am. 2014 Jul;136(1):EL1-7. doi: 10.1121/1.4881320.
A compressive-sensing approach called Sparse Representation Classifier (SRC) is applied to the classification of bottlenose dolphin whistles by type. The SRC algorithm constructs a dictionary of whistles from the collection of training whistles. In the classification phase, an unknown whistle is represented sparsely by a linear combination of the training whistles and then the call class can be determined with an l1-norm optimization procedure. Experimental studies conducted in this research reveal the advantages and limitations of the proposed method against some existing techniques such as K-Nearest Neighbors and Support Vector Machines in distinguishing different vocalizations.
一种名为稀疏表示分类器（SRC）的压缩感知方法被应用于宽吻海豚叫声类型的分类。SRC算法从训练叫声的集合中构建一个叫声字典。在分类阶段，一个未知叫声通过训练叫声的线性组合被稀疏表示，然后通过l1范数优化过程确定叫声类别。本研究中进行的实验研究揭示了该方法相对于一些现有技术（如K近邻和支持向量机）在区分不同发声方面的优点和局限性。
