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In this paper, we show that tensor compression techniques based on randomization and partial observations are very useful for spatial audio object coding. In this application, we aim at transmitting several audio signals called objects from a coder to a decoder. A common strategy is to transmit only the downmix of the objects along some small information permitting reconstruction at the decoder. In...
Upmixing consists in extracting audio objects out of their downmix, given some parameters computed beforehand at a coding stage. It is an important task in audio processing with many applications in the entertainment industry. One particularly successful approach for this purpose is to compress the audio objects through nonnegative matrix factorization (NMF) parameters at the coder, to be used for...
Nonnegative matrix factorization (NMF) is a widely used method for audio source separation. Additional constraints supporting e.g. temporal continuity or sparseness adapt NMF to the structure of audio signals even further. In this paper, we propose generalized NMF constraints which make use of prior information gathered for each component individually. In general, this information could be obtained...
Informed Source Separation (ISS) is a topic unifying the research fields of both source separation and source coding. Its main objective is to recover audio objects out of a mixture with a source separation step assisted by a set of compact parameters extracted with complete knowledge of the sources. ISS can be used for applications such as active listening and remixing of music (e.g. karaoke). In...
Nonnegative matrix factorization (NMF) is often used for source separation of audio signals. In most of these algorithms, the initialization step of the NMF, which has a strong impact on the separation performance, is based on random values or deterministic methods such as singular value decomposition (SVD). Another deterministic initialization approach, which is used e.g. for score-informed source...
Nonnegative matrix factorization (NMF) has become a very popular method in various signal processing applications. Supporting NMF with additional cost functions, so called priors, is very helpful to adapt the factorization to specific tasks. Additional priors are usually multiplied by fixed weights to adjust the influence of the prior. The question how to adapt these weights to the needs of specific...
Non-negative Matrix Factorization (NMF) is frequently used for audio source separation. One downside of the NMF is, that it is not able to capture temporal structure of sound events. NMF splits these events into different components. In this paper we present an extension to NMF, which is capable of representing sound events with temporal structure in only one component. We also present an algorithm,...
Nonnegative Matrix Factorization (NMF) is a well suited and widely used method for monaural sound source separation. It has been shown, that an additional cost term supporting temporal continuity can improve the separation quality [1]. We extend this model by adding a cost term, that penalizes large variations in the spectral dimension. We propose two different cost terms for this purpose and also...
In this paper, we analyze Logarithmic Cubic Vector Quantization (LCVQ), a novel type of gain-shape vector quantization (GSVQ). In LCVQ, the vector to be quantized is decomposed into a gain factor and a shape vector which is a normalized version of the input vector. Both components are quantized independently and transmitted to the decoder. Compared to other GSVQ approaches, in LCVQ the input vectors...
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