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 Classical music, unlike popular music, is usually recorded live with close microphone techniques. For this reason, isolated tracks are not available to create the final mixture/stream, and so the mixing process requires greater effort. Source separation methods are a potential solution to this problem. However, current algorithms are not fast enough to yield real-time separation in professional setups with dozens of microphones and sources. In this paper, we propose a fast approach consisting of a panning-based multichannel non-negative matrix factorization model to separate classical music. We tested the system on real professional recordings, where we were able to reach real-time with very low latency and promising quality.

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.

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.

Similar to non-negative matrix factorization (NMF), complex matrix factorization (CMF) can be used to decompose a given music recording into individual sound sources. In contrast to NMF, CMF models both the magnitude and phase of a source, which can improve the separation of overlapped partials. However, the shift-invariance for spectral templates enabling NMF-based methods to efficiently model vibrato in music is not available with CMF. Further, the estimation of an entire phase matrix for each source results in a high number of parameters in CMF, which often leads to poor local minima. In this paper we show that score information provides a source of prior knowledge rich enough to stabilize the CMF parameter estimation, without sacrificing its expressive power. As a second contribution, we present a shift-invariant extension to CMF bringing the vibrato-modeling capabilities of NMF to CMF. As our experiments demonstrate our proposed method consistently improves the separation quality for overlapped partials compared to score-informed NMF.

In this study, the authors present a novel voicing detection algorithm which employs the well‐known aperiodicity measure to detect voiced speech in signals contaminated with non‐stationary noise. The method computes a signal‐adaptive decision threshold which takes into account the current noise level, enabling voicing detection by direct comparison with the extracted aperiodicity. This adaptive threshold is updated at each frame by making a simple estimate of the current noise power, and thus is adapted to fluctuating noise conditions. Once the aperiodicity is computed, the method only requires a small number of operations, and enables its implementation in challenging devices (such as hearing aids) if an efficient approximation of the difference function is employed to extract the aperiodicity. Evaluation over a database of speech sentences degraded by several types of noise reveals that the proposed voicing classifier is robust against different noises and signal‐to‐noise ratios. In addition, to evaluate the applicability of the method for speech enhancement, a simple F 0 ‐based speech enhancement algorithm integrating the proposed classifier is implemented. The system is shown to achieve competitive results, in terms of objective measures, when compared with other well‐known speech enhancement approaches.

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.

This paper deals with the application of adaptive signal models for parametric audio coding. A fully parametric audio coder, which decomposes the audio signal into sinusoids, transients and noise, is here proposed. Adaptive signal models for sinusoidal, transient, and noise modeling are therefore included in the parametric scheme in order to achieve high-quality and low bit-rate audio coding. In this paper, a new sinusoidal modeling method based on a perceptual distortion measure is proposed. For transient modeling, a fast and effective method based on matching pursuit with a mixed dictionary is chosen. The residue of the previous models is analyzed as a noise-like signal. The proposed parametric audio coder allows high quality audio coding for one-channel audio signals at 16 kbits/s (average bit rate). A bit-rate scalable version of the parametric audio coder is also proposed in this work. Bit-rate scalability is intended for audio streaming applications, which are highly demanded nowadays. The performance of the proposed parametric audio coders (nonscalable and scalable coders) is assessed in comparison to widely used audio coders operating at similar bit rates.

The problem of fundamental frequency estimation is considered in the context of signals where the frequencies of the harmonics are not exact integer multiples of a fundamental frequency. This frequently occurs in audio signals produced by, for example, stiff-stringed musical instruments, and is sometimes referred to as inharmonicity. We derive a novel robust method based on the subspace orthogonality property of MUSIC and show how it may be used for analyzing audio signals. The proposed method is both more general and less complex than a straight-forward implementation of a parametric model of the inharmonicity derived from a physical instrument model. Additionally, it leads to more accurate estimates of the individual frequencies than the method based on the parametric inharmonicity model and a reduced bias of the fundamental frequency compared to the perfectly harmonic model.

Automatic speech/music discrimination is an important tool used in many multimedia applications, becoming a research topic of interest in the last years. This paper presents our last works in the speech/music discrimination field, aiming to improve the coding efficiency of standard audio coders (i.e. MP3, AAC) when speech and music signals are involved. In order to discriminate between speech and music, a fuzzy rules-based expert system is incorporated into the decision-taking stage of traditional speech/music discrimination systems. The knowledge base of the fuzzy expert system has been obtained by means of a typical genetic learning algorithm (the Pittsburgh algorithm). The proposed speech/music discrimination scheme manages the operation of an intelligent audio coder, which selects a GSM coder for speech frames and an AAC coder for music ones, resulting in a lower bit rate regarding the case of using a standardized audio coder (AAC in this work). Further, the intelligent audio coder has been designed aiming to obtain a similar subjective audio quality than AAC. GSM operates at 13 kbits/s, while in the experiments the bit rate specification for AAC has been 32 kbits/s for one-channel audio signals.

In this letter, we propose a novel matching pursuit-based method for transient modeling with application to parametric audio coding. The overcomplete dictionary for the matching pursuit is composed of wavelet functions that implement a wavelet-packet filter bank. The proposed transient modeling method is suitable to be integrated into a parametric audio coder based on the three-part model of sines, transients, and noise (STN model). Comparative analysis between wavelet and exponentially damped sinusoidal functions are shown in experimental results. The mean-squared-error performance of the proposed approach is better than that obtained with damped sinusoids.