musicaiz: A Python Library for Symbolic Music Generation, Analysis and Visualization

In this article, we present musicaiz, an object-oriented library for analyzing, generating and evaluating symbolic music. The submodules of the package allow the user to create symbolic music data from scratch, build algorithms to analyze symbolic music, encode MIDI data as tokens to train deep learning sequence models, modify existing music data and evaluate music generation systems. The evaluation submodule builds on previous work to objectively measure music generation systems and to be able to reproduce the results of music generation models. The library is publicly available online. We encourage the community to contribute and provide feedback

Music Boundary Detection using Convolutional Neural Networks: A comparative analysis of combined input features

The analysis of the structure of musical pieces is a task that remains a challenge for Artificial Intelligence, especially in the field of Deep Learning. It requires prior identification of structural boundaries of the music pieces. This structural boundary analysis has recently been studied with unsupervised methods and \textit{end-to-end} techniques such as Convolutional Neural Networks (CNN) using Mel-Scaled Log-magnitude Spectograms features (MLS), Self-Similarity Matrices (SSM) or Self-Similarity Lag Matrices (SSLM) as inputs and trained with human annotations. Several studies have been published divided into unsupervised and \textit{end-to-end} methods in which pre-processing is done in different ways, using different distance metrics and audio characteristics, so a generalized pre-processing method to compute model inputs is missing. The objective of this work is to establish a general method of pre-processing these inputs by comparing the inputs calculated from different pooling strategies, distance metrics and audio characteristics, also taking into account the computing time to obtain them. We also establish the most effective combination of inputs to be delivered to the CNN in order to establish the most efficient way to extract the limits of the structure of the music pieces. With an adequate combination of input matrices and pooling strategies we obtain a measurement accuracy $F_1$ of 0.411 that outperforms the current one obtained under the same conditions

Permutation Invariant Recurrent Neural Networks for Sound Source Tracking Applications

Many multi-source localization and tracking models based on neural networks use one or several recurrent layers at their final stages to track the movement of the sources. Conventional recurrent neural networks (RNNs), such as the long short-term memories (LSTMs) or the gated recurrent units (GRUs), take a vector as their input and use another vector to store their state. However, this approach results in the information from all the sources being contained in a single ordered vector, which is not optimal for permutation-invariant problems such as multi-source tracking. In this paper, we present a new recurrent architecture that uses unordered sets to represent both its input and its state and that is invariant to the permutations of the input set and equivariant to the permutations of the state set. Hence, the information of every sound source is represented in an individual embedding and the new estimates are assigned to the tracked trajectories regardless of their order.Comment: Accepted for publication at Forum Acusticum 202

Direction of Arrival Estimation of Sound Sources Using Icosahedral CNNs

In this paper, we present a new model for Direction of Arrival (DOA) estimation of sound sources based on an Icosahedral Convolutional Neural Network (CNN) applied over SRP-PHAT power maps computed from the signals received by a microphone array. This icosahedral CNN is equivariant to the 60 rotational symmetries of the icosahedron, which represent a good approximation of the continuous space of spherical rotations, and can be implemented using standard 2D convolutional layers, having a lower computational cost than most of the spherical CNNs. In addition, instead of using fully connected layers after the icosahedral convolutions, we propose a new soft-argmax function that can be seen as a differentiable version of the argmax function and allows us to solve the DOA estimation as a regression problem interpreting the output of the convolutional layers as a probability distribution. We prove that using models that fit the equivariances of the problem allows us to outperform other state-of-the-art models with a lower computational cost and more robustness, obtaining root mean square localization errors lower than 10{\deg} even in scenarios with a reverberation time $T_{60}$ of 1.5 s.Comment: Submitted to IEEE/ACM Transactions on Audio Speech and Language Processing. The code to reproduce this work can be found in our GitHub repository: https://github.com/DavidDiazGuerra/icoDO

gpuRIR: A python library for room impulse response simulation with GPU acceleration

The Image Source Method (ISM) is one of the most employed techniques to calculate acoustic Room Impulse Responses (RIRs), however, its computational complexity grows fast with the reverberation time of the room and its computation time can be prohibitive for some applications where a huge number of RIRs are needed. In this paper, we present a new implementation that dramatically improves the computation speed of the ISM by using Graphic Processing Units (GPUs) to parallelize both the simulation of multiple RIRs and the computation of the images inside each RIR. Additional speedups were achieved by exploiting the mixed precision capabilities of the newer GPUs and by using lookup tables. We provide a Python library under GNU license that can be easily used without any knowledge about GPU programming and we show that it is about 100 times faster than other state of the art CPU libraries. It may become a powerful tool for many applications that need to perform a large number of acoustic simulations, such as training machine learning systems for audio signal processing, or for real-time room acoustics simulations for immersive multimedia systems, such as augmented or virtual reality.Peer reviewe

IRAS 21391+5802: The Molecular Outflow and its Exciting Source

We present centimeter and millimeter observations of gas and dust around IRAS 21391+5802, an intermediate-mass source embedded in the core of IC 1396N. Continuum observations from 3.6 cm to 1.2 mm are used to study the embedded objects and overall distribution of the dust, while molecular line observations of CO, CS, and CH3OH are used to probe the structure and chemistry of the outflows in the region. The continuum emission at centimeter and millimeter wavelengths has been resolved into three sources separated about 15 arcsec from each other, and with one of them, BIMA 2, associated with IRAS 21391+5802. The dust emission around this source shows a very extended envelope, which accounts for most of the circumstellar mass of 5.1 Msun. This source is powering a strong molecular outflow, elongated in the E--W direction, which presents a complex structure and kinematics. While at high outflow velocities the outflow is clearly bipolar, at low outflow velocities the blueshifted and redshifted emission are highly overlapping, and the strongest emission shows a V-shaped morphology. The outflow as traced by CS and CH3OH exhibits two well differentiated and clumpy lobes, with two prominent northern blueshifted and redshifted clumps. The curved shape of the clumps and the spectral shape at these positions are consistent with shocked material. In addition, CS and CH3OH are strongly enhanced toward these positions with respect to typical quiescent material abundances in other star-forming regions.Comment: 41 pages, including 11 figures, accepted for publication in ApJ (July 1); available at http://www.am.ub.es/~robert/Papers.html#las

A strategy for scaling up access to comprehensive care in adults with Chagas disease in endemic countries: The Bolivian Chagas Platform

BACKGROUND: Bolivia has the highest prevalence of Chagas disease (CD) in the world (6.1%), with more than 607,186 people with Trypanosoma cruzi infection, most of them adults. In Bolivia CD has been declared a national priority. In 2009, the Chagas National Program (ChNP) had neither a protocol nor a clear directive for diagnosis and treatment of adults. Although programs had been implemented for congenital transmission and for acute cases, adults remained uncovered. Moreover, health professionals were not aware of treatment recommendations aimed at this population, and research on CD was limited; it was difficult to increase awareness of the disease, understand the challenges it presented, and adapt strategies to cope with it. Simultaneously, migratory flows that led Bolivian patients with CD to Spain and other European countries forced medical staff to look for solutions to an emerging problem. INTERVENTION: In this context, thanks to a Spanish international cooperation collaboration, the Bolivian platform for the comprehensive care of adults with CD was created in 2009. Based on the establishment of a vertical care system under the umbrella of ChNP general guidelines, six centres specialized in CD management were established in different epidemiological contexts. A common database, standardized clinical forms, a and a protocolized attention to adults patients, together with training activities for health professionals were essential for the model success. With the collaboration and knowledge transfer activities between endemic and non-endemic countries, the platform aims to provide care, train health professionals, and create the basis for a future expansion to the National Health System of a proven model of care for adults with CD. RESULTS: From 2010 to 2015, a total of 26,227 patients were attended by the Platform, 69% (18,316) were diagnosed with T. cruzi, 8,567 initiated anti-parasitic treatment, more than 1,616 health professionals were trained, and more than ten research projects developed. The project helped to increase the number of adults with CD diagnosed and treated, produce evidence-based clinical practice guidelines, and bring about changes in policy that will increase access to comprehensive care among adults with CD. The ChNP is now studying the Platform's health care model to adapt and implement it nationwide. CONCLUSIONS: This strategy provides a solution to unmet demands in the care of patients with CD, improving access to diagnosis and treatment. Further scaling up of diagnosis and treatment will be based on the expansion of the model of care to the NHS structures. Its sustainability will be ensured as it will build on existing local resources in Bolivia. Still human trained resources are scarce and the high staff turnover in Bolivia is a limitation of the model. Nevertheless, in a preliminary two-years-experience of scaling up this model, this limitations have been locally solved together with the health local authorities

Enhancer Remodeling during Adaptive Bypass to MEK Inhibition Is Attenuated by Pharmacologic Targeting of the P-TEFb Complex

Targeting the dysregulated BRaf-MEK-ERK pathway in cancer has increasingly emerged in clinical trial design. Despite clinical responses in specific cancers using inhibitors targeting BRaf and MEK, resistance develops often involving non-genomic adaptive bypass mechanisms. Inhibition of MEK1/2 by trametinib in triple negative breast cancer (TNBC) patients induced dramatic transcriptional responses, including upregulation of receptor tyrosine kinases (RTKs) comparing tumor samples before and after one week of treatment. In preclinical models MEK inhibition induced genome-wide enhancer formation involving the seeding of BRD4, MED1, H3K27 acetylation and p300 that drives transcriptional adaptation. Inhibition of P-TEFb associated proteins BRD4 and CBP/p300 arrested enhancer seeding and RTK upregulation. BRD4 bromodomain inhibitors overcame trametinib resistance, producing sustained growth inhibition in cells, xenografts and syngeneic mouse TNBC models. Pharmacological targeting of P-TEFb members in conjunction with MEK inhibition by trametinib is an effective strategy to durably inhibit epigenomic remodeling required for adaptive resistance