Matrix factorization techniques have proven to be useful in many unsupervised learning applications. Such techniques have been recently applied to Non Intrusive Load Monitoring (NILM), the process of breaking down the total electric consumption of a building into consumptions of individual appliances. While several studies addressed the NILM problem for small-scale buildings, only few studies considered the problem for large buildings, where the signals exhibit significantly different behavior. To overcome the unaddressed difficulties of processing high frequency current signals that are measured in large buildings, we propose a novel technique called Independent-Variation Matrix Factorization (IVMF), which expresses an observation matrix as the product of two matrices: the signature and the activation. Motivated by the nature of the current signals, it uses a regularization term on the temporal variations of the activation matrix and a positivity constraint, and the columns of the signature matrix are constrained to lie in a specific set. To solve the resulting optimization problem, we rely on an alternating minimization strategy involving dual optimization and quasi-Newton algorithms. The algorithm is tested against Independent Component Analysis (ICA) and Semi Nonnegative Matrix Factorization (SNMF) on a synthetic source separation problem and on a realistic NILM application for large commercial buildings. We show that IVMF outperforms competing methods and is particularly appropriate to recover positive sources that have a strong temporal dependency and sources whose variations are independent from each other.

In this paper one investigates the merit of partial user annotation for music transcription using a PLCA-based model. The original algorithm, called Blind Harmonic Adaptive Decomposition (BHAD), provides an estimation of the polyphonic pitch content of the input signal in an entirely unsupervised manner. In this paper, one studies how the performance of the BHAD algorithm can be further improved by involving a user by means of a partial annotation. This user input allows for a better model initialisation with adapted or learned spectral envelope models. Furthermore, it is studied how a fine control of the convergence rate of some parameters can better exploit this additional information. It is then shown that this partial annotation can bring an improvement of up to 3% on the transcription of the remaining file.

Recently, new methods for smart decomposition of time-frequency representations of audio have been proposed in order to address the problem of automatic music transcription. However those techniques are not necessarily suitable for notes having variations of both pitch and spectral envelope over time. The HALCA (Harmonic Adaptive Latent Component Analysis) model presented in this article allows considering those two kinds of variations simultaneously. Each note in a constant-Q transform is locally modeled as a weighted sum of fixed narrowband harmonic spectra, spectrally convolved with some impulse that defines the pitch. All parameters are estimated by means of the expectation-maximization (EM) algorithm, in the framework of Probabilistic Latent Component Analysis. Interesting priors over the parameters are also introduced in order to help the EM algorithm converging towards a meaningful solution. We applied this model for automatic music transcription: the onset time, duration and pitch of each note in an audio file are inferred from the estimated parameters. The system has been evaluated on two different databases and obtains very promising results.

Dimension reduction techniques such as Nonnegative Tensor Factorization are now classical for both source separation and estimation of multiple fundamental frequencies in audio mixtures. Still, few studies jointly addressed these tasks so far, mainly because separation is often based on the Short Term Fourier Transform (STFT) whereas recent music analysis algorithms are rather based on the Constant-Q Transform (CQT). The CQT is practical for pitch estimation because a pitch shift amounts to a translation of the CQT representation, whereas it produces a scaling of the STFT. Conversely, no simple inversion of the CQT was available until recently, preventing it from being used for source separation. Benefiting from advances both in the inversion of the CQT and in statistical modeling, we show how recent techniques designed for music analysis can also be used for source separation with encouraging results, thus opening the path to many crossovers between separation and analysis.

Numerous methods have been developed for the time-frequency analysis and smart decomposition of audio signals. However, these techniques are not consistently suitable for real music signals where each note presents continuous variations of both pitch and spectral envelope. This paper presents a new model for analyzing the harmonic structures of an audio signal that can jointly handle those two types of variations. Each note in a constant-Q transform is modeled as a weighted sum of narrowband parametric spectra, and positive deconvolution is performed to estimate the model parameters, in the framework of probabilistic latent component analysis. The algorithm has been tested in a task of monopitch estimation. The very promising results highlight the reliability and the robustness of the model.