There is an overwhelming number of medications that can affect the morphology of the beating heart by delaying the ventricular depolarization and repolarization processes, resulting in an elongated QT interval. This circumstance, in turn, can lead to ventricular arrhythmias, which can result in the patient’s death, as was overwhelmingly the case at the beginning of the coronavirus pandemic. In this regard, this document presents an innovative expert system-based application devoted to estimating the level of risk of fatal ventricular arrhythmia as a consequence of QT prolonging treatments. For this purpose, the use of a fuzzy rule-based system has been considered for the decision-making process, establishing three different risk levels regarding the QT interval obtained from an electrocardiogram, its evolution from a basal electrocardiogram, and other significant clinical information of the patient such as age, sex, among others. This novel expert system-based application has been designed by considering the criteria of several specialist physicians from four different Spanish private and public hospitals. The proposed application can be especially important in primary health care centers, where the absence of specialists is remarkable because it provides clinical staff with a powerful tool for estimating the level of risk of fatal ventricular arrhythmia with 96.5% accuracy when comparing its decisions to those of specialist physicians, which could imply a significant reduction in the risk of death due to fatal ventricular arrhythmias in patients with QT prolonging treatment. In this respect, the developed application is currently used in some primary health care centers in Spain.

Abstract Sound source separation (SSS) is a fundamental problem in audio signal processing, aiming to recover individual audio sources from a given mixture. A promising approach is multichannel non-negative matrix factorization (MNMF), which employs a Gaussian probabilistic model encoding both magnitude correlations and phase differences between channels through spatial covariance matrices (SCM). In this work, we present a dedicated hardware architecture implemented on field programmable gate arrays (FPGAs) for efficient SSS using MNMF-based techniques. A novel decorrelation constraint is presented to facilitate the factorization of the SCM signal model, tailored to the challenges of multichannel source separation. The performance of this FPGA-based approach is comprehensively evaluated, taking advantage of the flexibility and computational capabilities of FPGAs to create an efficient real-time source separation framework. Our experimental results demonstrate consistent, high-quality results in terms of sound separation.

This paper addresses the problem of multichannel source separation in ambisonics signals combining two powerful approaches, multichannel NMF with recent single-channel deep learning (DL) based spectrum inference. Individual source spectra are estimated from the zero-order (omnidirectional) spherical harmonic (SH) signal with a Masker-Denoiser twin network, able to model long-term temporal patterns of a musical piece. The initialized source spectrograms are used within a SHdomain spatial covariance mixing model based on multichannel non-negative matrix factorization (MNMF) that predicts the spatial characteristics of each source in order to refine the network prediction. The proposed framework is evaluated on the task of singing voice separation with a large dataset of simulated ambisonics signals using several setups. Experimental results show that our joint DL+SH-MNMF method outperforms both the individual monophonic DL-based separation, baseline multichannel MNMF and SH domain beamforming classical methods.

Nonnegative matrix factorization (NMF) has been traditionally considered a promising approach for audio source separation. While standard NMF is only suited for single-channel mixtures, extensions to consider multi-channel data have been also proposed. Among the most popular alternatives, multichannel NMF (MNMF) and further derivations based on constrained spatial covariance models have been successfully employed to separate multi-microphone convolutive mixtures. This letter proposes a MNMF extension by considering a mixture model with Ray-Space-transformed signals, where magnitude data successfully encodes source locations as frequency-independent linear patterns. We show that the MNMF algorithm can be seamlessly adapted to consider Ray-Space-transformed data, providing competitive results with recent state-of-the-art MNMF algorithms in a number of configurations using real recordings.

This work addresses the problem of multichannel source separation combining two powerful approaches, multichannel spectral factorization with recent monophonic deep learning (DL) based spectrum inference. Individual source spectra at different channels are estimated with a Masker-Denoiser twin network, able to model long-term temporal patterns of a musical piece. The monophonic source spectrograms are used within a spatial covariance mixing model based on complex-valued multichannel non-negative matrix factorization (CMNMF) that predicts the spatial characteristics of each source. The proposed framework is evaluated on the task of singing voice separation with a large multichannel dataset. Experimental results show that our joint DL+CMNMF method outperforms both the individual monophonic DL-based separation and the multichannel CMNMF baseline methods.

This paper presents an algorithm for multichannel sound source separation using explicit modeling of level and time differences in source spatial covariance matrices (SCM). We propose a novel SCM model in which the spatial properties are modeled by the weighted sum of direction of arrival (DOA) kernels. DOA kernels are obtained as the combination of phase and level difference covariance matrices representing both time and level differences between microphones for a grid of predefined source directions. The proposed SCM model is combined with the NMF model for the magnitude spectrograms. Opposite to other SCM models in the literature, in this work, source localization is implicitly defined in the model and estimated during the signal factorization. Therefore, no localization preprocessing is required. Parameters are estimated using complex-valued nonnegative matrix factorization with both Euclidean distance and Itakura-Saito divergence. Separation performance of the proposed system is evaluated using the two-channel SiSEC development dataset and four channels signals recorded in a regular room with moderate reverberation. Finally, a comparison to other state-of-the-art methods is performed, showing better achieved separation performance in terms of SIR and perceptual measures.

In this article, we present an online score following framework designed to deal with automatic accompaniment. The proposed framework is based on spectral factorization and online Dynamic Time Warping (DTW) and has two separated stages: preprocessing and alignment. In the first one, we convert the score into a reference audio signal using a MIDI synthesizer software and we analyze the provided information in order to obtain the spectral patterns (i.e., basis functions) associated to each score unit. In this work, a score unit represents the occurrence of concurrent or isolated notes in the score. These spectral patterns are learned from the synthetic MIDI signal using a method based on Non-negative Matrix Factorization (NMF) with Beta-divergence, where the gains are initialized as the ground-truth transcription inferred from the MIDI. On the second stage, a non-iterative signal decomposition method with fixed spectral patterns per score unit is used over the magnitude spectrogram of the input signal resulting in a distortion matrix that can be interpreted as the cost of the matching for each score unit at each frame. Finally, the relation between the performance and the musical score times is obtained using a strategy based on online DTW, where the optimal path is biased by the speed of interpretation. Our system has been evaluated and compared to other systems, yielding reliable results and performance.

In a monaural polyphonic context, music transcription and specifically, multiple-F0 estimation systems have achieved promising results in the last decade. However, most of these systems present intermittent misses of pitch within a note or inaccurate definitions about onsets and offsets due to frame-by-frame analysis. In this paper, we propose a multiple-F0 estimation system which extracts a set of active pitches at each frame (analysis frame) but note tracking is performed defining temporal intervals by an accurate onset detector. Our system shows promising results, in terms of onset and multiple-F0 estimation, to be evaluated using real-world and synthesized polyphonic music recordings taken from MAPS music database.

Harmonic decompositions are a powerful tool dealing with polyphonic music signals in some potential applications such as music visualization, music transcription and instrument recognition. The usefulness of a harmonic decomposition relies on the design of a proper harmonic dictionary. Music scene-adaptive harmonic atoms have been used with this purpose. These atoms are adapted to the musical instruments and to the music scene, including aspects related with the venue, musician, and other relevant acoustic properties. In this paper, an unsupervised process to obtain music scene-adaptive spectral patterns for each MIDI-note is proposed. Furthermore, the obtained harmonic dictionary is applied to note-event detection with matching pursuits. In the case of a music database that only consists of one-instrument signals, promising results (high accuracy and low error rate) have been achieved for note-event detection.