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检索条件"主题词=Kernel Additive Modelling"

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Does k Matter? k-NN Hubness Analysis for kernel additive modelling Vocal Separation 14th

Does k Matter? k-NN Hubness Analysis for Kernel Additive Mod...

引用

14th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA)

作者： Yela, Delia Fano Stowell, Dan Sandler, Mark Queen Mary Univ London Mile End Rd London E1 4NS England

ISBN: (纸本)9783319937649;9783319937632

kernel additive modelling (KAM) is a framework for source separation aiming to explicitly model inherent properties of sound sources to help with their identification and separation. KAM separates a given source by applying robust statistics on the selection of time-frequency bins obtained through a source-specific kernel, typically the k-NN function. Even though the parameter k appears to be key for a successful separation, little discussion on its influence or optimisation can be found in the literature. Here we propose a novel method, based on graph theory statistics, to automatically optimise k in a vocal separation task. We introduce the k-NN hubness as an indicator to find a tailored k at a low computational cost. Subsequently, we evaluate our method in comparison to the common approach to choose k. We further discuss the influence and importance of this parameter with illuminating results.

关键词： Source separation kernel additive modelling Graph theory Music processing Vocal separation

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Interference reduction in music recordings combining kernel additive modelling and Non-Negative Matrix Factorization

Interference reduction in music recordings combining Kernel ...

引用

2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017

作者： Fano Yela, Delia Ewert, Sebastian Fitzgerald, Derry Sandler, Mark Queen Mary University of London United Kingdom Nimbus Centre Cork Institute of Technology Ireland

ISBN: (纸本)9781509041176

In live and studio recordings unexpected sound events often lead to interferences in the signal. For non-stationary interferences, sound source separation techniques can be used to reduce the interference level in the recording. In this context, we present a novel approach combining the strengths of two algorithmic families: NMF and KAM. The recent KAM approach applies robust statistics on frames selected by a source-specific kernel to perform source separation. Based on semi-supervised NMF, we extend this approach in two ways. First, we locate the interference in the recording based on detected NMF activity. Second, we improve the kernel-based frame selection by incorporating an NMF-based estimate of the clean music signal. Further, we introduce a temporal context in the kernel, taking some musical structure into account. Our experiments show improved separation quality for our proposed method over a state-of-the-art approach for interference reduction. © 2017 IEEE.

关键词： Interference Reduction kernel additive modelling Non-Negative Matrix Factorization Source separation

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Interference reduction in music recordings combining kernel additive modelling and Non-Negative Matrix Factorization

Interference reduction in music recordings combining Kernel ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Delia Fano Yela Sebastian Ewert Derry FitzGerald Mark Sandler Queen Mary University of London UK Nimbus Centre Cork Institute of Technology Ireland

ISBN: (纸本)9781509041183

关键词： Source separation kernel additive modelling Non-Negative Matrix Factorization Interference Reduction

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Dialogue Enhancement using kernel additive modelling 23

Dialogue Enhancement using Kernel Additive Modelling

引用

23nd Signal Processing and Communications Applications Conference (SIU)

作者： Kirbiz, Serap Liutkus, Antoine Cemgil, A. Taylan MEF Univ Istanbul Turkey CNRS Inria Loria Speech TeamUMR 7503 Villers Les Nancy France Bogazici Univ Istanbul Turkey

ISBN: (纸本)9781467373869

It is a major problem for the sound engineers to find the right balance between the dialogue signals and the ambient sources. This problem also makes one of the main causes of the audience concerns. The audience wants to arrange the sound balance based on their personal preferences, listening environment and their hearing. In this work, a method is proposed for enhancing the dialogue signals in stereo recordings that consist of more than one source. The kernel additive modelling that has been used successfully in sound source separation is used to extract the dialogues and the ambient sources from the movie sounds. The separated dialogue and ambient sources can later be upmixed by the user to make a personal mix. The separation performance of the proposed method is evaluated on the sounds generated by mixing the sources which were taken from the only dialogue and only music parts of the movies. It has been shown that the kernel additive modelling (KAM) based method can be successfully used for dialogue enhancement.

关键词： Dialogue enhancement kernel additive modelling upmixing

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Scalable audio separation with light kernel additive modelling 40

Scalable audio separation with light Kernel Additive Modelli...

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Liutkus, Antoine Fitzgerald, Derry Rafii, Zafar Inria Speech Processing Team Villers-lès-Nancy France NIMBUS Centre Cork Institute of Technology Ireland Gracenote Media Technology Lab EmeryvilleCA United States

ISBN: (纸本)9781467369978

Recently, kernel additive modelling (KAM) was proposed as a unified framework to achieve multichannel audio source separation. Its main feature is to use kernel models for locally describing the spectrograms of the sources. Such kernels can capture source features such as repetitivity, stability over time and/or frequency, self-similarity, etc. KAM notably subsumes many popular and effective methods from the state of the art, including REPET and harmonic/percussive separation with median filters. However, it also comes with an important drawback in its initial form: its memory usage badly scales with the number of sources. Indeed, KAM requires the storage of the full-resolution spectrogram for each source, which may become prohibitive for full-length tracks or many sources. In this paper, we show how it can be combined with a fast compression algorithm of its parameters to address the scalability issue, thus enabling its use on small platforms or mobile devices. © 2015 IEEE.

关键词： kernel additive modelling randomized algorithms sound source separation

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