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In this paper we describe a semi-supervised algorithm to segment bird vocalizations using matrix factorization and Rényi entropy based mutual information. Singular value decomposition (SVD) is applied on pooled time-frequency representations of bird vocalizations to learn basis vectors. By utilizing only a few of the bases, a compact feature representation is obtained for input test data. Rényi entropy...
Bird activity detection is the task of determining if a bird sound is present in a given audio recording. This paper describes a bird activity detector which utilises a support vector machine (SVM) with a dynamic kernel. Dynamic kernels are used to process sets of feature vectors having different cardinalities. Probabilistic sequence kernel (PSK) is one such dynamic kernel. The PSK converts a set...
In this paper, we describe an unsupervised method to segment birdcalls from the background in bioacoustic recordings. The method utilizes information derived from both source features as well as system features. Three types of source features are extracted from the linear prediction residual signal, and Mel frequency cepstral coefficients are extracted from the system features. The source features...
In this paper, we describe an unsupervised, species independent method to segment birdcalls from the background in bio-acoustic recordings. The method follows a two-pass approach. An initial segmentation is performed utilizing K-means clustering. This provides labels to train Gaussian mixture acoustic models, which are built using Mel frequency cepstral coefficients. Using the acoustic models, the...
Articulation errors are common in children but may also persist in adulthood. These errors decrease the speech intelligibility and hamper the ability of a person to communicate properly. A speech language pathologist carefully examines the speech to identify the articulation error patterns. It is exhaustive and time consuming process. This information about the patterns of articulation disorders is...
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