Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music
Authors: Haizi Yu, Lav R. Varshney
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 EXPERIMENTS MUS-ROVER II’s main use case is to produce personalized syllabi that are roadmaps to learning the input style (customized paths to Mount Parnassus). [...] We illustrate two syllabi in Table 1, which compares the first ten (1-gram) rules in a faster (γ = 0.5) syllabus and a slower one (γ = 0.1)." and "Let’s take a closer look at the two syllabi in Table 1. The specifications (left) and hierarchies (right) of the four common rules are illustrated in Table 2. |
| Researcher Affiliation | Academia | Haizi Yu Department of Computer Science University of Illinois at Urbana-Champaign Urbana, IL 61801, USA haiziyu7@illinois.edu Lav R. Varshney Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana, IL 61801, USA varshney@illinois.edu |
| Pseudocode | Yes | A.3 ALGORITHM FOR CONCEPTUAL HIERARCHY Algorithm 1: Algorithm for computing the conceptual hierarchy |
| Open Source Code | No | The paper provides a link to a dataset ("Harmonia’s Music XML corpus of Bach’s chorales (https://harmonia.illiacsoftware.com/)") but does not explicitly state that the code for the methodology described in this paper is open-source or provide a link to it. |
| Open Datasets | Yes | We use the same dataset that comprises 370 C scores of Bach’s 4-part chorales." and "We thank Professor Heinrich Taube, President of Illiac Software, Inc., for providing Harmonia’s Music XML corpus of Bach’s chorales (https://harmonia.illiacsoftware.com/), |
| Dataset Splits | No | The paper describes a self-learning loop and uses a dataset for training but does not specify explicit training/validation/test splits (e.g., percentages or counts) or cross-validation setup. |
| Hardware Specification | No | The paper does not explicitly describe the hardware (e.g., specific GPU or CPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper makes a brief mention of 'python nan variable' but does not provide specific version numbers for software dependencies or libraries used in their implementation. |
| Experiment Setup | Yes | There are two hyper-parameters δ and γ in the optimization problem (3), whose detailed usage in syllabus customization will be discussed later in Sec. 6. At a high level, we often pre-select γ before the loop to express a user’s satisfaction level: a smaller γ signifies a meticulous user who is harder to satisfy; the threshold δ upper bounds the entropic difficulty of the rules, and is adaptively adjusted through the loop: it starts from a small value (easy rules first), and auto-increases whenever the feasible set of (3) is empty (gradually increases the difficulty when mastering the current level). |