arXiv subjects

Sungho Lee

Publications and source records attributed to Sungho Lee.

At least 19 recordsLinked to original sources

arXiv2026

LipCoder: Voice-Enabled Coding Toolkit

AI-assisted programming environments have accelerated software development, giving rise to new paradigms like vibe coding. However, their benefits remain largely inaccessible to visually impaired programmers, as existing screen readers and assistive tools offer limited support for these emerging workflows. We introduce LipCoder, a voice-centric programming toolkit designed to deliver editor-level functionality through auditory and speech-based interfaces. LipCoder offers features comprising speech feedback and earcon cues for comprehension and validation, as well as natural language input for navigation and modification. In an exploratory evaluation, 5 visually impaired programmers performed a series of coding tasks comparing LipCoder with a baseline of VSCode, Copilot, and VoiceOver. Quantitative trends and qualitative feedback point to directions for auditory-first design that may broaden accessibility in speech-driven coding environments.

cs.HC↗

arXiv2026

Exploring the Design Space of Representation Learning for Audio Transformations

Neural audio representation learning has enabled a range of content-oriented applications, but the resulting features remain limited for tasks involving audio processing. Furthermore, it is not obvious what processing-aware representations should capture: the processing itself, abstracted away from source content, or the processed audio that retains it. Existing approaches implicitly commit to one or the other and also differ in their models, data, and evaluation, obscuring which design choices drive their behavior. We address both questions within a unified framework of three objectives: processing consistency, description alignment, and equivariance via forward prediction. We compare all combinations of the objectives under a controlled setup and reveal their relative strengths and interactions. Our framework produces both a transformation embedding and a processed-audio embedding, and we find that the two play complementary roles: distance-based tasks favor the former, while probe-based tasks favor the latter. Combined with improvements in network architecture and training pipeline, our representations outperform prior baselines across retrieval, probe-based evaluation, and style transfer.

cs.SD↗

arXiv2026

The Roasting Marshmallows Program with IGRINS on Gemini South V: Atmosphere of MASCARA-1b is Enriched in Refractory Elements

Ultra-hot Jupiters (UHJs; $T_{\rm eq} \gtrsim 2000$ K) enable simultaneous detection of volatile (ice-forming) and refractory (rock-forming) species in planetary atmospheres, providing a powerful diagnostic of planet formation and atmospheric processing. We present a comprehensive high-resolution cross-correlation spectroscopy (HRCCS) analysis of the UHJ MASCARA-1b ($T_{\rm eq} \approx 2600$ K) using the IGRINS and IGRINS-2 spectrographs. We detect robust (SNR$>$4) signals from H$_2$O, CO, OH, Fe I, Mg I, Ca I, and Ti I, marking the most complete atmospheric inventory of MASCARA-1b to date. Using a chemically consistent atmospheric inference framework, we constrain elemental abundances to a typical precision of $\approx$0.2 dex, retrieving a solar atmospheric metallicity ([M/H]$_\odot$ $= 0.07^{+0.17}_{-0.13}$ $\approx 1.2\times$ solar), a C/O ratio (C/O $= 0.65^{+0.08}_{-0.08}$) consistent with solar value (C/O $=$ 0.59), an enhanced refractory abundance ([R/H]$_\odot$ $= 0.40^{+0.23}_{-0.17} \approx 2.5\times$ solar; $\approx 3.8\times$ stellar), and a moderately super-solar refractory-to-volatile ratio ([R/V]$_\odot$ $= 0.36^{+0.11}_{-0.09}$ $\approx 2.3\times$ solar). Comparison with formation models suggests that MASCARA-1b most likely accreted material between the soot-H$_2$O or H$_2$O-CO snowlines (at 68$\%$ confidence). We additionally find stellar values for atmospheric Ti/Mg and Ca/Mg ratios (at 68$\%$ confidence). The Mg/Fe is also found to be consistent with stellar value at 95$\%$ confidence. Therefore, we do not find strong indication of nightside cold trapping in MASCARA-1b. As homogeneous refractory-to-volatile measurements expand across the UHJ population, particularly with upcoming Extremely Large Telescopes, these diagnostics will enable statistically robust tests of emerging trends in giant planet formation and atmospheric evolution.

astro-ph.EP↗

arXiv2026

Rethinking Speech Representation Aggregation in Speech Enhancement: A Phonetic Mutual Information Perspective

Recent speech enhancement (SE) models increasingly leverage self-supervised learning (SSL) representations for their rich semantic information. Typically, intermediate features are aggregated into a single representation via a lightweight adaptation module. However, most SSL models are not trained for noise robustness, which can lead to corrupted semantic representations. Moreover, the adaptation module is trained jointly with the SE model, potentially prioritizing acoustic details over semantic information, contradicting the original purpose. To address this issue, we first analyze the behavior of SSL models on noisy speech from an information-theoretic perspective. Specifically, we measure the mutual information (MI) between the corrupted SSL representations and the corresponding phoneme labels, focusing on preservation of linguistic contents. Building upon this analysis, we introduce the linguistic aggregation layer, which is pre-trained to maximize MI with phoneme labels (with optional dynamic aggregation) and then frozen during SE training. Experiments show that this decoupled approach improves Word Error Rate (WER) over jointly optimized baselines, demonstrating the benefit of explicitly aligning the adaptation module with linguistic contents.

cs.SD↗

arXiv2026

Quantum Metrology under Coarse-Grained Measurement

While quantum metrology enables measurement precision beyond classical limits, its performance is often susceptible to experimental imperfections. Most prior studies have focused on imperfections in quantum states and operations. Here, we investigate the effect of coarse graining in quantum measurement through both theoretical analysis and experimental demonstration. Using an interferometer with a squeezed vacuum and a laser input, we analyze how coarse graining in homodyne detection affects the precision of phase estimation. We evaluate the Fisher information under various coarse-graining conditions and determine, in each case, an optimal estimation strategy that saturates the Cram\'{e}r-Rao bound. Remarkably, even extremely coarse-grained measurement -- with only two bins -- enables phase estimation beyond the standard quantum limit and even achieves a precision that follows the Heisenberg scaling. We experimentally demonstrate quantum-enhanced phase estimation under coarse-grained homodyne detection. To determine an optimal estimation strategy, we employ the method of moments and present calibration procedures that enable its application to general experimental settings. Using only two bins, we observe a quantum enhancement of 1.2 dB compared to the classical method using the ideal measurement, improving towards 3.8 dB as the bin number increases. These results highlight a practical pathway to achieving quantum enhancement under the presence of severe experimental imperfections.

quant-ph↗

arXiv2025

Differentiable Acoustic Radiance Transfer

Geometric acoustics is an efficient framework for room acoustics modeling, governed by the canonical time-dependent rendering equation. Acoustic radiance transfer (ART) solves the equation by discretization, modeling time- and direction-dependent energy exchange between surface patches with flexible material properties. We introduce DART, an efficient, differentiable implementation of ART that enables gradient-based optimization of material properties. We evaluate DART on a simpler variant of acoustic field learning that aims to predict energy responses for novel source-receiver configurations. Experimental results demonstrate that DART generalizes better under sparse measurement scenarios than existing signal processing and neural network baselines, while maintaining simplicity and full interpretability. We open-source our implementation.

cs.SD↗

arXiv2025

Reverse Engineering of Music Mixing Graphs with Differentiable Processors and Iterative Pruning

Reverse engineering of music mixes aims to uncover how dry source signals are processed and combined to produce a final mix. We extend the prior works to reflect the compositional nature of mixing and search for a graph of audio processors. First, we construct a mixing console, applying all available processors to every track and subgroup. With differentiable processor implementations, we optimize their parameters with gradient descent. Then, we repeat the process of removing negligible processors and fine-tuning the remaining ones. This way, the quality of the full mixing console can be preserved while removing approximately two-thirds of the processors. The proposed method can be used not only to analyze individual music mixes but also to collect large-scale graph data that can be used for downstream tasks, e.g., automatic mixing. Especially for the latter purpose, efficient implementation of the search is crucial. To this end, we present an efficient batch-processing method that computes multiple processors in parallel. We also exploit the "dry/wet" parameter of the processors to accelerate the search. Extensive quantitative and qualitative analyses are conducted to evaluate the proposed method's performance, behavior, and computational cost.

cs.SD↗

arXiv2025

Few-step Adversarial Schr\"{o}dinger Bridge for Generative Speech Enhancement

Deep generative models have recently been employed for speech enhancement to generate perceptually valid clean speech on large-scale datasets. Several diffusion models have been proposed, and more recently, a tractable Schr\"odinger Bridge has been introduced to transport between the clean and noisy speech distributions. However, these models often suffer from an iterative reverse process and require a large number of sampling steps -- more than 50. Our investigation reveals that the performance of baseline models significantly degrades when the number of sampling steps is reduced, particularly under low-SNR conditions. We propose integrating Schr\"odinger Bridge with GANs to effectively mitigate this issue, achieving high-quality outputs on full-band datasets while substantially reducing the required sampling steps. Experimental results demonstrate that our proposed model outperforms existing baselines, even with a single inference step, in both denoising and dereverberation tasks.

cs.SD↗

arXiv2025

Near-Infrared Spectroscopy with IGRINS-2 for Studying Multiple Stellar Populations in Globular Clusters

Recent advancements in near-infrared (NIR) spectroscopy have opened new opportunities for studying multiple stellar populations in globular clusters (GCs), particularly for newly discovered clusters in the inner Milky Way. While optical spectroscopy has traditionally played a primary role in detailed chemical abundance studies of GCs, the increasing discovery of GCs in highly reddened environments underscores the need for robust NIR spectroscopic methods. To evaluate the utility of high-resolution NIR spectroscopy for studying multiple stellar populations, we observed six stars in M5, a well-studied halo GC, using the recently commissioned IGRINS-2 spectrograph on the Gemini-North telescope. Our chemical abundance measurements in the NIR wavelength range show good agreement with those derived from high-resolution optical spectroscopy, with minor systematic offsets in elements such as Na and Mg. In addition, the measured chemical abundance ratios clearly reproduce the distinctive patterns of multiple stellar populations, including the Na-O anti-correlation. The ability of NIR spectroscopy to measure C, N, and O abundances with high precision further enhances its utility for studying chemical properties of stars and GCs. Our findings demonstrate that IGRINS-2 and similar instruments have significant potential to advance our understanding of GC formation, stellar chemical evolution, and the evolutionary history of the Milky Way.

astro-ph.GA↗

arXiv2025

An Early Look at the Performance of IGRINS-2 at Gemini-North with Application to the ultrahot Jupiter, WASP-33 b

Ground-based high-resolution spectroscopy enables precise molecular detections and velocity-resolved atmospheric dynamics, offering a distinct advantage over low-resolution methods for exoplanetary atmospheric studies. IGRINS-2, the successor to IGRINS, features improved throughput and enhanced sensitivity to carbon monoxide by shifting its $\textit{K}$-band coverage by 36 nm to longer wavelengths. IGRINS is a near-infrared high-resolution spectrograph mounted at McDonald, Lowell, and Gemini-South observatories. Our order-drop test shows this added range improves the CO cross-correlation signal-to-noise ratio (SNR) by 2$-$3%, confirming a measurable but modest sensitivity gain. To evaluate its performance, we attempt to investigate the atmospheric characteristics of WASP-33 b. Observations were conducted on 2024 January 7 for a total of 2.43 hours; This includes 1.46 hours in the pre-eclipse phase to capture the planet's thermal emission spectrum. We successfully detect clear cross-correlation signals from molecular species in the dayside atmosphere of WASP-33 b with a combined SNR of 7.4. More specifically, we capture CO, H$_{2}$O, and OH with SNRs of 6.3, 4.7, and 4.2, respectively. These results are consistent with previous studies and demonstrate that IGRINS-2 is well-suited for detailed investigation of exoplanetary atmospheres. We anticipate that future observations with IGRINS-2 will further advance our understanding of exoplanetary atmospheres.

astro-ph.EP↗

arXiv2025

10 Years of Archival High-Resolution NIR Spectra: The Raw and Reduced IGRINS Spectral Archive (RRISA)

The Immersion GRating INfrared Spectrometer (IGRINS) is a compact, high-resolution (R~45,000) near-infrared spectrograph spanning 1.45 to 2.45 um in a single exposure. We introduce the Raw and Reduced IGRINS Spectral Archive (RRISA), which provides public data access for all non-proprietary IGRINS data taken at McDonald Observatory's Harlan J. Smith Telescope, the Lowell Discovery Telescope (formerly Discovery Channel Telescope), and Gemini South. RRISA provides access to raw files, reduced data products, and cross-matched IGRINS targets with the SIMBAD, 2MASS, Gaia DR3, APOGEE2 DR17, and PASTEL catalogs. We also introduce version 3 of the IGRINS data reduction pipeline, IGRINS PLP v3, which implements an improved cosmic ray correction, pattern noise removal, and a new flexure correction that reduces telluric residuals. RRISA and supporting information can be found at http://igrinscontact.github.io.

astro-ph.IM↗

arXiv2025

TokenSynth: A Token-based Neural Synthesizer for Instrument Cloning and Text-to-Instrument

Recent advancements in neural audio codecs have enabled the use of tokenized audio representations in various audio generation tasks, such as text-to-speech, text-to-audio, and text-to-music generation. Leveraging this approach, we propose TokenSynth, a novel neural synthesizer that utilizes a decoder-only transformer to generate desired audio tokens from MIDI tokens and CLAP (Contrastive Language-Audio Pretraining) embedding, which has timbre-related information. Our model is capable of performing instrument cloning, text-to-instrument synthesis, and text-guided timbre manipulation without any fine-tuning. This flexibility enables diverse sound design and intuitive timbre control. We evaluated the quality of the synthesized audio, the timbral similarity between synthesized and target audio/text, and synthesis accuracy (i.e., how accurately it follows the input MIDI) using objective measures. TokenSynth demonstrates the potential of leveraging advanced neural audio codecs and transformers to create powerful and versatile neural synthesizers. The source code, model weights, and audio demos are available at: https://github.com/KyungsuKim42/tokensynth

cs.SD↗

arXiv2024

GRAFX: An Open-Source Library for Audio Processing Graphs in PyTorch

We present GRAFX, an open-source library designed for handling audio processing graphs in PyTorch. Along with various library functionalities, we describe technical details on the efficient parallel computation of input graphs, signals, and processor parameters in GPU. Then, we show its example use under a music mixing scenario, where parameters of every differentiable processor in a large graph are optimized via gradient descent. The code is available at https://github.com/sh-lee97/grafx.

cs.SD↗

arXiv2024

Wavespace: A Highly Explorable Wavetable Generator

Wavetable synthesis generates quasi-periodic waveforms of musical tones by interpolating a list of waveforms called wavetable. As generative models that utilize latent representations offer various methods in waveform generation for musical applications, studies in wavetable generation with invertible architecture have also arisen recently. While they are promising, it is still challenging to generate wavetables with detailed controls in disentangling factors within the latent representation. In response, we present Wavespace, a novel framework for wavetable generation that empowers users with enhanced parameter controls. Our model allows users to apply pre-defined conditions to the output wavetables. We employ a variational autoencoder and completely factorize its latent space to different waveform styles. We also condition the generator with auxiliary timbral and morphological descriptors. This way, users can create unique wavetables by independently manipulating each latent subspace and descriptor parameters. Our framework is efficient enough for practical use; we prototyped an oscillator plug-in as a proof of concept for real-time integration of Wavespace within digital audio workspaces (DAWs).

cs.SD↗

arXiv2024

Searching For Music Mixing Graphs: A Pruning Approach

Music mixing is compositional -- experts combine multiple audio processors to achieve a cohesive mix from dry source tracks. We propose a method to reverse engineer this process from the input and output audio. First, we create a mixing console that applies all available processors to every chain. Then, after the initial console parameter optimization, we alternate between removing redundant processors and fine-tuning. We achieve this through differentiable implementation of both processors and pruning. Consequently, we find a sparse mixing graph that achieves nearly identical matching quality of the full mixing console. We apply this procedure to dry-mix pairs from various datasets and collect graphs that also can be used to train neural networks for music mixing applications.

cs.SD↗

arXiv2023

Ensuring End-to-End Security with Fine-grained Access Control for Connected and Autonomous Vehicles

As advanced V2X applications emerge in the connected and autonomous vehicle (CAV), the data communications between in-vehicle end-devices and outside nodes increase, which make the end-to-end (E2E) security to in-vehicle end-devices as the urgent issue to be handled. However, the E2E security with fine-grained access control still remains as a challenging issue for resource-constrained end-devices since the existing security solutions require complicated key management and high resource consumption. Therefore, we propose a practical and secure vehicular communication protocol for the E2E security based on a new attribute-based encryption (ABE) scheme. In our scheme, the outsourced computation is provided for encryption, and the computation cost for decryption constantly remains small, regardless of the number of attributes. The policy privacy can be ensured by the proposed ABE to support privacy-sensitive V2X applications, and the existing identity-based signature for outsourced signing is newly reconstructed. Our scheme achieves the confidentiality, message authentication, identity anonymity, unlinkability, traceability, and reconfigurable outsourced computation, and we also show the practical feasibility of our protocol via the performance evaluation.

cs.CR↗

arXiv2023

Beat-Aligned Spectrogram-to-Sequence Generation of Rhythm-Game Charts

In the heart of "rhythm games" - games where players must perform actions in sync with a piece of music - are "charts", the directives to be given to players. We newly formulate chart generation as a sequence generation task and train a Transformer using a large dataset. We also introduce tempo-informed preprocessing and training procedures, some of which are suggested to be integral for a successful training. Our model is found to outperform the baselines on a large dataset, and is also found to benefit from pretraining and finetuning.

cs.LG↗

arXiv2023

Yet Another Generative Model For Room Impulse Response Estimation

Recent neural room impulse response (RIR) estimators typically comprise an encoder for reference audio analysis and a generator for RIR synthesis. Especially, it is the performance of the generator that directly influences the overall estimation quality. In this context, we explore an alternate generator architecture for improved performance. We first train an autoencoder with residual quantization to learn a discrete latent token space, where each token represents a small time-frequency patch of the RIR. Then, we cast the RIR estimation problem as a reference-conditioned autoregressive token generation task, employing transformer variants that operate across frequency, time, and quantization depth axes. This way, we address the standard blind estimation task and additional acoustic matching problem, which aims to find an RIR that matches the source signal to the target signal's reverberation characteristics. Experimental results show that our system is preferable to other baselines across various evaluation metrics.

cs.SD↗