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Supervised and semi-supervised source separation algorithms based on non-negative matrix factorization have been shown to be quite effective. However, they require isolated training examples of one or more sources, which is often difficult to obtain. This limits the practical applicability of these algorithms. We examine the problem of efficiently utilizing(More)
In this paper we present a novel approach for isolating and removing sounds from dense monophonic mixtures. The approach is user-based, and requires the presentation of a guide sound that mimics the desired target the user wishes to extract. The guide sound can be simply produced from a user by vocalizing or otherwise replicating the target sound marked for(More)
In recent years, there has been a great deal of work in mod-eling audio using non-negative matrix factorization and its probabilistic counterparts as they yield rich models that are very useful for source separation and automatic music transcription. Given a sound source, these algorithms learn a dictionary of spectral vectors to best explain it. This(More)
We present a semi-supervised source separation methodology to denoise speech by modeling speech as one source and noise as the other source. We model speech using the recently proposed non-negative hidden Markov model, which uses multiple non-negative dictionaries and a Markov chain to jointly model spectral structure and temporal dynamics of speech. We(More)
We present an algorithm based on probabilistic latent component analysis and employ it for relative pitch estimation of multiple instruments in polyphonic music. A multilayered positive deconvolution is performed concurrently on mixture constant-Q transforms to obtain a relative pitch track and timbral signature for each instrument. Initial experimental(More)
Non-negative spectrogram factorization algorithms such as probabilistic latent component analysis (PLCA) have been shown to be quite powerful for source separation. When training data for all of the sources are available, it is trivial to learn their dictionaries beforehand and perform supervised source separation in an online fashion. However, in many(More)
In this work, we investigate a method for score-informed source separation using Probabilistic Latent Component Analysis (PLCA). We present extensive test results that give an indication of the performance of the method, its strengths and weaknesses. For this purpose, we created a test database that has been made available to the public, in order to(More)
In applications such as audio denoising, music transcription, music remixing, and audio-based forensics, it is desirable to decompose a single-channel recording into its respective sources. One of the current most effective class of methods to do so is based on non-negative matrix factorization and related latent variable models. Such techniques, however ,(More)
Traditional audio editing tools do not facilitate the task of separating a single mixture recording (e.g. pop song) into its respective sources (e.g. drums, vocal, etc.). Such ability, however, would be very useful for a wide variety of audio applications such as music remixing, audio denoising, and audio-based forensics. To address this issue, we present(More)