7 research outputs found
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 of 0.411 that outperforms the
current one obtained under the same conditions
VRDMG: Vocal Restoration via Diffusion Posterior Sampling with Multiple Guidance
Restoring degraded music signals is essential to enhance audio quality for
downstream music manipulation. Recent diffusion-based music restoration methods
have demonstrated impressive performance, and among them, diffusion posterior
sampling (DPS) stands out given its intrinsic properties, making it versatile
across various restoration tasks. In this paper, we identify that there are
potential issues which will degrade current DPS-based methods' performance and
introduce the way to mitigate the issues inspired by diverse diffusion guidance
techniques including the RePaint (RP) strategy and the Pseudoinverse-Guided
Diffusion Models (GDM). We demonstrate our methods for the vocal
declipping and bandwidth extension tasks under various levels of distortion and
cutoff frequency, respectively. In both tasks, our methods outperform the
current DPS-based music restoration benchmarks. We refer to
\url{http://carlosholivan.github.io/demos/audio-restoration-2023.html} for
examples of the restored audio samples
A Comparison of Deep Learning Methods for Timbre Analysis in Polyphonic Automatic Music Transcription
Automatic music transcription (AMT) is a critical problem in the field of music information retrieval (MIR). When AMT is faced with deep neural networks, the variety of timbres of different instruments can be an issue that has not been studied in depth yet. The goal of this work is to address AMT transcription by analyzing how timbre affect monophonic transcription in a first approach based on the CREPE neural network and then to improve the results by performing polyphonic music transcription with different timbres with a second approach based on the Deep Salience model that performs polyphonic transcription based on the Constant-Q Transform. The results of the first method show that the timbre and envelope of the onsets have a high impact on the AMT results and the second method shows that the developed model is less dependent on the strength of the onsets than other state-of-the-art models that deal with AMT on piano sounds such as Google Magenta Onset and Frames (OaF). Our polyphonic transcription model for non-piano instruments outperforms the state-of-the-art model, such as for bass instruments, which has an F-score of 0.9516 versus 0.7102. In our latest experiment we also show how adding an onset detector to our model can outperform the results given in this work
Identificación e impacto clÃnico de las interacciones farmacológicas potenciales en prescripciones médicas del Hospital ISSSTE Pachuca, México
el objetivo de este trabajo fue identificar la presencia de las interacciones farmacológicas potenciales en las prescripciones médicas del servicio de consulta externa (recetas médicas), y determinar su impacto clÃnico, en el hospital ISSSTE Pachuca. Se realizó un estudio retrospectivo, transversal, observacional y comparativo; se analizaron las prescripciones, a través de las recetas médicas en los servicios clÃnicos de medicina interna, medicina familiar y cardiologÃa. Se determinó la frecuencia de interacciones farmacológicas potenciales y se evaluó su significancia clÃnica a través de indicadores relacionados con: inicio de acción, severidad y la documentación, a través de la información proporcionada en la literatura cientÃfica. Los resultados muestran que la frecuencia de interacciones potenciales fue mayor cuando se prescribieron 2 y 4 medicamentos, predominaron las interacciones lentas (inicio de acción), moderadas (severidad) y posibles (documentación). Las interacciones más frecuentes fueron las de analgésicos no esteroideos e inhibidores de la enzima convertidora de angiotensina y beta bloqueadores. Este estudio, permitió conocer datos de frecuencia de interacciones farmacológicas potenciales, asà como su significancia clÃnica en el hospital en estudio, con la finalidad de prevenir su presencia y de apoyar el uso racional de los medicamentos
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
Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)
In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field