Briggs et al. (2012b) proposed to represent audio Bioacoustic monitoring is a rapidly growing field, where the recordings of bird sound in the multi-instance multi-label goal is to learn about organisms such as birds and marine (MIML) framework (Zhou et al. 2012). In this formulation, mammals, by applying signal processing and machine learning an audio recording is transformed to a spectrogram, to audio recordings. In this paper, we consider the problem then automatically segmented into a collection of regions of class discovery from bird bioacoustics data. Given believed to be distinct utterances of bird sound. Each segment a large collection of audio recordings of birds (and other is then described by a feature vector that characterizes sounds in the environment), our goal is to automatically select its shape, texture, and time/frequency profiles. A recording a subset of recordings to be manually labeled by human is represented as a set of segment feature vectors (instances).