Daniel Szelogowski

Daniel Szelogowski
Capitol Technology University · Computer Science

Master of Science

About

8
Publications
1,859
Reads
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0
Citations
Citations since 2016
8 Research Items
0 Citations
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Introduction
Director of choral and orchestral music, vocalist, pianist, cellist, guitarist, bassist, and computer scientist/engineer. Growing researcher in the fields of music, education, computer science, data science, and cognitive science especially.
Additional affiliations
August 2021 - present
Parker High School
Position
  • Managing Director
June 2020 - present
Outschool
Position
  • Remote Educator
Description
  • Remote instruction of general music and computer science courses for middle and high school age students, providing AP and college-level curricula in music theory, introductory and intermediate programming languages, data structures, machine learning, discrete math, computer science principles, and applied instrumental instruction
Education
September 2022 - May 2023
University of Wisconsin - Green Bay
Field of study
  • Applied Bioinformatics
August 2022 - April 2024
Capitol Technology University
Field of study
  • Artificial Intelligence
February 2021 - May 2021
University of Wisconsin - Milwaukee
Field of study
  • General Studies

Publications

Publications (8)
Thesis
Full-text available
Musical form analysis is a rigorous task that frequently challenges the expertise of human analysts and signal processing algorithms alike. While numerous systems have been proposed to perform the tasks of musical segmentation, genre classification, and single-label segment classification in popular music, none have specifically focused on the anal...
Preprint
Full-text available
Currently, most graph compression algorithms focus on in-memory compression (such as for web graphs) – few are feasible for external compression, and there is no generalized approach to either task. These compressed representations are versatile and can be applied to a great number of different applications, with the most common being social networ...
Preprint
Full-text available
Current computational-emotion research has focused on applying acoustic properties to analyze how emotions are perceived mathematically or used in natural language processing machine learning models. While recent interest has focused on analyzing emotions from the spoken voice, little experimentation has been performed to discover how emotions are...
Preprint
Full-text available
With web and mobile platforms becoming more prominent devices utilized in data analysis, there are currently few systems which are not without flaw. In order to increase the performance of these systems and decrease errors of data oversimplification, we seek to understand how other programming languages can be used across these platforms which prov...
Preprint
Full-text available
Chunking data is obviously no new concept; however, I had never found any data structures that used chunking as the basis of their implementation. I figured that by using chunking alongside concurrency, I could create an extremely fast run-time in regards to particular methods as searching and/or sorting. By using chunking and concurrency to my adv...
Preprint
Full-text available
Musical form analysis is a rigorous task that frequently challenges the expertise of human analysts and signal processing algorithms alike. While numerous systems have been proposed to perform the tasks of musical segmentation, genre classification, and single-label segment classification in popular music, none have specifically focused on the anal...
Preprint
Full-text available
Current AI-generated music lacks fundamental principles of good compositional techniques. By narrowing down implementation issues both programmatically and musically, we can create a better understanding of what parameters are necessary for a generated composition nearly indistinguishable from that of a master composer.
Preprint
Full-text available
Current open source applications which allow for cross-platform data visualization of OLAP cubes feature issues of high overhead and inconsistency due to data oversimplification. To improve upon this issue, there is a need to cut down the number of pipelines that the data must travel between for these aggregation operations and create a single, uni...

Questions

Question (1)
Question
I'm trying to make a multichannel neural network that has 2 input models that pipeline into a single model (see image: https://i.stack.imgur.com/b6V7x.png ).
I need the first (top/left) channel to take in one tensor, and the second channel to take in three tensors. Of course, in doing so, I'm running into the issue of ambiguous data cardinality, because I'm comparing the output to the y_train set which is only 1 tensor.
Here's the error I'm getting:
ValueError: Data cardinality is ambiguous: x sizes: 1, 3 y sizes: 2
What's the best way to make this work?
Here's essentially what I have at the moment for fitting the data to the model:
model_history = trmodel.fit((np.array([model_images[0]], dtype=np.float32), np.array([model_images[1], model_images[2], model_images[3]], dtype=np.float32)), np.array(labels_seconds, dtype=np.float32), batch_size=32, epochs=2000, validation_data=(labels_seconds,), callbacks=[checkpoint])
It's been some time since I've worked with Keras, and I've never needed a multichannel network until now, so my apologies for my rustiness in the process. I can post the full code if that would help, also.