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Computational Auditory Scene Analysis (CASA) is challenging problem for which many different approaches have been proposed. These approaches can be based on statistical and signal processing methods such as Independent Component Analysis or can be based on our current knowledge about human auditory perception. Learning happens at the boundary interactions between prior knowledge and incoming data. Separating complex mixtures of sound sources such as music requires a complex interplay between prior knowledge and analysis of incoming data. Many approaches to CASA can also be broadly categorized as either model-based or grouping-based. Although it is known that our perceptual-system utilizes both of these types of processing, building such systems computationally has been challenging. As a result most existing systems either rely on prior source models or are solely based on grouping cues. In this chapter the authors argue that formulating this integration problem as clustering based on similari- ties between time-frequency atoms provides an expressive yet disciplined approach to building sound source characterization and separation systems and evaluating their performance. After describing the main components of such an architecture, the authors describe a concrete realization that is based on spectral clustering of a sinusoidal representation. They show how this approach can be used to model both traditional grouping cues such as frequency and amplitude continuity as well as other types of in- formation and prior knowledge such as onsets, harmonicity and timbre-models for specific instruments.