Learn More
This paper presents a family of probabilistic latent variable models that can be used for analysis of nonnegative data. We show that there are strong ties between nonnegative matrix factorization and this family, and provide some straightforward extensions which can help in dealing with shift invariances, higher-order decompositions and sparsity(More)
In this paper we describe a methodology for model-based single channel separation of sounds. We present a sparse latent variable model that can learn sounds based on their distribution of time/frequency energy. This model can then be used to extract known types of sounds from mixtures in two scenarios. One being the case where all sound types in the mixture(More)
In this paper we describe a model developed for the analysis of acoustic spectra. Unlike decom-positions techniques that can result in difficult to interpret results this model explicitly models spectra as distributions and extracts sets of additive and semantically useful components that facilitate a variety of applications ranging from source separation,(More)
Detecting unsolicited content and the spammers who create it is a long-standing challenge that affects all of us on a daily basis. The recent growth of richly-structured social networks has provided new challenges and opportunities in the spam detection landscape. Motivated by the Tagged.com social network, we develop methods to identify spammers in(More)
This paper presents an overview of an intelligence platform we have built to address threat hunting and incident investigation use-cases in the cyber security domain. Specifically, we focus on User and Entity Behavior Analytics (UEBA) modules that track and monitor behaviors of users, IP addresses and devices in an enterprise. Anomalous behavior is(More)
This paper presents an advanced building energy management system (aBEMS) that employs advanced methods of whole-building performance monitoring combined with statistical methods of learning and data analysis to enable identification of both gradual and discrete performance erosion and faults. This system assimilated data collected from multiple sources,(More)
With the recent attention towards audio processing in the time-frequency domain we increasingly encounter the problem of missing data within that representation. In this paper we present an approach that allows us to recover missing values in the time-frequency domain of audio signals. The presented approach is able to deal with real-world polyphonic(More)
In this work, we propose a new framework for learning mixture models from continuous data. Gaus-sian Mixture Models (GMMs) are commonly used for this task and are popular among practitioners because of their sound statistical foundation and the availability of an efficient learning algorithm [2]. However, the underlying assumption about the normally(More)