Trajectory of Fifths in Music Data Mining

Kania, Dariusz; Kania, Paulina; Łukaszewicz, Tomasz

doi:10.1109/access.2021.3049266

Cited by 8 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To formalize this work, we adopted a social change methodology known as Collective Impact, originally defined as “the commitment of a group of important actors from different sectors to a common agenda for solving a specific social problem,” by Kania et al in 2011 15 . We note that our process conformed to the subsequent 2022 revision of the framework that centered equity as a prerequisite and systems-level change as a goal 16 . Collective impact has been effective in facilitating large-scale change (e.g., a successful regional watershed restoration program, implementation of a citywide obesity prevention program in youth) 17 and offered a structured yet flexible framework to guide systemwide transformation.…”

Section: The Task Force To Address Racism and Biasmentioning

confidence: 85%

Designing a Road Map for Action to Address Bias and Racism Within a Large Academic Medical Center

Butts,

Abner,

Hess

et al. 2023

Acad Med

View full text Add to dashboard Cite

The convergence of the COVID-19 pandemic, the Black Lives Matter Movement, and the public anguish and outrage resulting from the murder of George Floyd in 2020 intensified the commitment of many health care institutions to pursue racial and social justice and achieve health equity. The authors describe the Road Map for Action to Address Racism, which was developed to unify and systematize antiracism efforts across the Mount Sinai Health System. A 51-member Task Force to Address Racism, comprising faculty, staff, students, alumni, health system leaders, and trustees, developed recommendations to achieve the goal of becoming an antiracist and equitable health care and learning institution by intentionally addressing all forms of racism and promoting greater diversity, inclusion, and equity for its workforce and community. Grounded in the principles of Collective Impact, the Task Force developed a set of 11 key strategies to effect systemwide change. The strategies affected all aspects of the organization: business systems and financial operations, delivery of care, workforce development and training, leadership development, medical education, and community engagement. The authors describe Road Map implementation, currently in process, including the appointment of strategy leaders, evolution of a governance structure integrating stakeholders from across the health system, development of an evaluation framework, communication and engagement efforts, and process measures and progress to date. Lessons learned include the importance of recognizing the work of dismantling racism as integral to, not apart from, the institution’s day-to-day work, and the need for specialized expertise and a significant investment of time to coordinate Road Map implementation. Going forward, rigorous assessment of quantitative and qualitative outcomes and a commitment to sharing successes and challenges will be critical to eradicating systems that have perpetuated inequities in the biomedical sciences and medicine and in the delivery of health care.

show abstract

Section: The Task Force To Address Racism and Biasmentioning

confidence: 85%

Designing a Road Map for Action to Address Bias and Racism Within a Large Academic Medical Center

Butts,

Abner,

Hess

et al. 2023

Acad Med

View full text Add to dashboard Cite

show abstract

“…In [22], a music piece is described using the "trajectory of fifths," which improves music data mining techniques by examining the variability of the music signature over time. The trajectory of fifths is beneficial for assessing music tonality and genre classification, as validated by experiments demonstrating its applicability through statistical analysis.…”

Section: Related Workmentioning

confidence: 99%

Design and Implementation of Automatic Music Composition System Based on Deep Learning

Wang

2024

jes

View full text Add to dashboard Cite

Recently, there has been a notable increase in interest surrounding the utilization of Deep Learning Approaches for AMCS in recent times. However, current methods often lack coherence and authenticity in the generated music. This study proposes a new approach that utilizes RNNs to bridge the gap between traditional compositional methods and modern deep learning techniques. The goal is to produce expressive and coherent musical pieces. Our methodology involves designing and implementing a customized RNN architecture that can effectively capture the complex temporal dependencies present in musical sequences. We experiment with different types of including LSTM networks, RNNs, and GRUs, to overcome challenges such as vanishing gradients and better model longer-term dependencies in music. To train the neural network efficiently and improve model convergence, various deep learning optimizers are utilized in our system. Specifically, we use SGD optimizer to improve the hyper parameters of LSTM and GRUs. The data for training is converted into MIDI format and analyzed, with music lines being identified through a similarity matrix technique. The MIDI data is then prepared for use in the LSTM and GRUs networks. The resulting music is assessed using both objective measures, such as mean squared error, and subjective approaches. This research adds to the advancements in automatic music composition by demonstrating the capability of RNNs in capturing and producing complex musical arrangements.

show abstract

“…The Characteristic Point of the Music Signature (CPMS) is the tip of the polar vector obtained as the sum of the polar vectors {𝑘 ⃗ 𝑖 : 𝑖 = 1, 2, …, 12} comprising a given music signature. Its Cartesian coordinates (x CPMS , y CPMS ) can be calculated using the following formulas [37]:…”

Section: Def 2 the Characteristic Point Of The Music Signature (Cpms)mentioning

confidence: 99%

“…For the purposes of the study discussed herein we resorted to a set of basic coefficients known from the analysis of the human CoP (Center of Pressure) trajectories [38]: the length of the trajectory, the area under the trajectorycalculated with respect to the trajectory's center or the center of the coordinate system, as well as the mean deviation from the center of the trajectory or the center of the coordinate system. Definitions of these coefficients, adjusted to the needs of the analysis of the trajectories of fifths, were presented below [37], [38]:…”

Section: Quantification Of the Trajectory Of Fifthsmentioning

confidence: 99%

A Music Classification Approach Based on the Trajectory of Fifths

Łukaszewicz

Kania

2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

In this paper we examine the applicability of the trajectory of fifths as a source of knowledge in automated music classification processes. The study shows that such trajectories provide valuable information concerning the harmonic structure of a given piece of music. The results of the conducted experiments indicate that even basic coefficients quantifying the trajectory of fifths (e.g. the length of the trajectory) can be utilized as feature variables in music classification algorithms. The performed experiments involved the computation of the trajectories of fifths for the music pieces from two genre groups -rock/pop and jazz, calculation of their basic coefficients, and using these coefficients as feature variables for various rock/pop -jazz classifiers based on popular machine learning algorithms, i.e. Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Linear Discriminant Analysis, Gaussian Naïve Bayes, and C-SVM. The tests evaluating the performance of the established classifiers have shown that in all of the considered cases the mean balanced accuracies were greater than 0.900, reaching the maximum of 0.936 (std=0.019) for the C-SVM classifier and the minimum of 0.910 (std=0.017) for the classification using the Decision Tree algorithm. Given the basic nature of the utilized coefficients and the relative simplicity of the applied classification models, the achieved results seem very promising. They strongly encourage further research concerning the use of the trajectory of fifths in different music classification algorithmsespecially evaluation of its potential in the multi-class or multi-label genre classification scenarios.INDEX TERMS music classification, computer music analysis, music information retrieval, music data mining.

show abstract

Trajectory of Fifths in Music Data Mining

Cited by 8 publications

References 28 publications

Designing a Road Map for Action to Address Bias and Racism Within a Large Academic Medical Center

Designing a Road Map for Action to Address Bias and Racism Within a Large Academic Medical Center

Design and Implementation of Automatic Music Composition System Based on Deep Learning

A Music Classification Approach Based on the Trajectory of Fifths

Contact Info

Product

Resources

About