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In this work we propose novel joint and sequential multimodal approaches for the task of single channel audio source separation in videos. This is done within the popular non-negative matrix factorization framework using information about the sounding object's motion. Specifically, we present methods that utilize non-negative least squares formulation to couple motion and audio information. The proposed...
We consider example-guided audio source separation approaches, where the audio mixture to be separated is supplied with source examples that are assumed matching the sources in the mixture both in frequency and time. These approaches were successfully applied to the tasks such as source separation by humming, score-informed music source separation, and music source separation guided by covers. Most...
In this paper we tackle the problem of single channel audio source separation driven by descriptors of the sounding object's motion. As opposed to previous approaches, motion is included as a soft-coupling constraint within the nonnegative matrix factorization framework. The proposed method is applied to a multimodal dataset of instruments in string quartet performance recordings where bow motion...
We propose a novel informed source separation method for audio object coding based on a recent sampling theory for smooth signals on graphs. Assuming that only one source is active at each time-frequency point, we compute an ideal map indicating which source is active at each time-frequency point at the encoder. This map is then sampled with a compressive graph signal sampling strategy that guarantees...
Nonnegative matrix or tensor factorization is a very popular approach for audio source separation. One important problem in nonnegative tensor factorization (NTF) in the context of user-guided audio source separation is the necessity to manually assign the NTF components to audio sources in order to be able to enforce prior information on the sources during the estimation process. In this paper, two...
The paradigm of using a very simple encoder and a sophisticated decoder for compression of signals became popular with the theory of distributed coding and it has been exercised for the compression of various types of signals such as images and video. The theory of compressive sampling later introduced a similar concept but with the focus on guarantees of signal recovery using sparse and low rank...
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