Search results for: Antoine Liutkus
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Source separation, which consists in decomposing data into meaningful structured components, is an active research topic in many fields including music signal processing. In this paper, we introduce the Positive α-stable (PαS) distributions to model the latent sources, which are a subclass of the stable distributions family. They notably permit us to model random variables that are both nonnegative...
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This paper introduces a new method for single-channel denoising that sheds new light on classical early developments on this topic that occurred in the 70's and 80's with Wiener filtering and spectral subtraction. Operating both in the short-time Fourier transform domain, these methods consist in estimating the power spectral density (PSD) of the noise without speech. Then, the clean speech signal...
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This paper presents an accelerated version of positive semidefinite tensor factorization (PSDTF) for blind source separation. PSDTF works better than nonnegative matrix factorization (NMF) by dropping the arguable assumption that audio signals can be whitened in the frequency domain by using short-term Fourier transform (STFT). Indeed, this assumption only holds true in an ideal situation where each...
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In this paper, we focus on the problem of sound source localization and we propose a technique that exploits the known and arbitrary geometry of the microphone array. While most probabilistic techniques presented in the past rely on Gaussian models, we go further in this direction and detail a method for source localization that is based on the recently proposed α-stable harmonizable processes. They...
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In this paper, we focus on modeling multichannel audio signals in the short-time Fourier transform domain for the purpose of source separation. We propose a probabilistic model based on a class of heavy-tailed distributions, in which the observed mixtures and the latent sources are jointly modeled by using a certain class of multivariate alpha-stable distributions. As opposed to the conventional Gaussian...
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In this paper, we propose a simple user-assisted method for the recovery of repeating patterns in time and frequency which can occur in audio mixtures. Here, the user selects a region in a log-frequency spectrogram from which they seek to recover the underlying pattern, such as a repeating chord masked by a cough. Cross-correlation is then performed between the selected region and the spectrogram,...
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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...
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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...
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We propose the Multi-resolution Common Fate Transform (MCFT), a signal representation that increases the separability of audio sources with significant energy overlap in the time-frequency domain. The MCFT combines the desirable features of two existing representations: the invertibility of the recently proposed Common Fate Transform (CFT) and the multi-resolution property of the cortical stage output...
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IEEE/ACM Transactions on Audio, Speech, and Language Processing > 2016 > 24 > 9 > 1556 - 1568
We propose a method to unmix multichannel audio signals into their different constitutive spatial objects. To achieve this, we characterize an audio object through both a spatial and a spectro-temporal modeling. The particularity of the spatial model we pick is that it neither assumes an object has only one underlying source point, nor does it attempt to model the complex room acoustics. Instead,...
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IEEE/ACM Transactions on Audio, Speech, and Language Processing > 2016 > 24 > 9 > 1652 - 1664
This article addresses the problem of multichannel audio source separation. We propose a framework where deep neural networks (DNNs) are used to model the source spectra and combined with the classical multichannel Gaussian model to exploit the spatial information. The parameters are estimated in an iterative expectation-maximization (EM) fashion and used to derive a multichannel Wiener filter. We...
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Audio source separation models are typically evaluated using objective separation quality measures, but rigorous statistical methods have yet to be applied to the problem of model comparison. As a result, it can be difficult to establish whether or not reliable progress is being made during the development of new models. In this paper, we provide a hypothesis-driven statistical analysis of the results...
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This article addresses the problem of multichannel music separation. We propose a framework where the source spectra are estimated using deep neural networks and combined with spatial covariance matrices to encode the source spatial characteristics. The parameters are estimated in an iterative expectation-maximization fashion and used to derive a multichannel Wiener filter. We evaluate the proposed...
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Lecture Notes in Computer Science > Latent Variable Analysis and Signal Separation > Non-negativity; Image Processing Applications > 498-505
We address the issue of source separation in a particular informed configuration where both the sources and the mixtures are assumed to be known during a so-called encoding stage. This knowledge enables the computation of a side information which ought to be small enough to be watermarked in the mixtures. At the decoding stage, the sources are no longer assumed to be known, only the mixtures and the...
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In this paper we present a novel source separation method aiming to overcome the difficulty of modelling non-stationary signals. The method can be applied to mixtures of musical instruments with frequency and/or amplitude modulation, e.g. typically caused by vibrato. It is based on a signal representation that divides the complex spectrogram into a grid of patches of arbitrary size. These complex...
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We propose a projection-based method for the unmixing of multichannel audio signals into their different constituent spatial objects. Here, spatial objects are modelled using a unified framework which handles both point sources and diffuse sources. We then propose a novel methodology to estimate and take advantage of the spatial dependencies of an object. Where previous research has processed the...
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IEEE Signal Processing Letters > 2015 > 22 > 12 > 2289 - 2293
Matrix factorization (MF) models have been widely used in data analysis. Even though they have been shown to be useful in many applications, classical MF models often fall short when the observed data are impulsive and contain outliers. In this study, we present MF, a MF model with -stable observations. Stable distributions are a family of heavy-tailed distributions that is particularly...
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We consider the problem of robust automatic speech recognition (ASR) in the context of the CHiME-3 Challenge. The proposed system combines three contributions. First, we propose a deep neural network (DNN) based multichannel speech enhancement technique, where the speech and noise spectra are estimated using a DNN based regressor and the spatial parameters are derived in an expectation-maximization...
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Nonnegative matrix factorization (NMF) is an effective and popular low-rank model for nonnegative data. It enjoys a rich background, both from an optimization and probabilistic signal processing viewpoint. In this study, we propose a new cost-function for NMF fitting, which is introduced as arising naturally when adopting a Cauchy process model for audio waveforms. As we recall, this Cauchy process...
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Automatic food intake monitoring can be significantly beneficial in the fight against obesity and weight management in our society today. Different sensing modalities have been used in several research efforts to accomplish automatic food intake monitoring with acoustic sensors being the most common. In this study, we explore the ability to learn spectral patterns of food intake acoustics from a clean...
