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Results for Audio processing




Results for Audio processing

User-guided one-shot deep model adaptation for music source separation
RESEARCH PAPER / Oct 2021 / Audio processing, Neural network, Machine learning/ Deep learning /Artificial Intelligence
Music source separation is the task of isolating individual instruments which are mixed in a musical piece. This task is particularly challenging, and even state-of-the-art models can hardly generalize to unseen test data. Nevertheless, prior knowledge about individual sources can be used to better adapt a generic source separation model...
Audio Style Transfer
RESEARCH PAPER / Apr 2018 / Audio Processing, Machine/Deep Learning/AI
Style transfer' among images has recently emerged as a very active research topic, fuelled by the power of convolution neural networks (CNNs), and has become fast a very popular technology in social media. This paper investigates the analogous problem in the audio domain: How to transfer the style of a...
Comparative study of example-guided audio source separation approaches based on nonnegative matrix factorization
RESEARCH PAPER / Sep 2017 / Audio Processing, Machine/Deep Learning/AI
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...
Motion Informed Source Separation
RESEARCH PAPER / Mar 2017 / Audio Processing, Machine/Deep Learning/AI
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...
INFORMED SOURCE SEPARATION BY COMPRESSIVE GRAPH SIGNAL SAMPLING
RESEARCH PAPER / Mar 2017 / Audio Processing, Machine/Deep Learning/AI
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...

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