Brandon Carone | Cognitive Science and Music Technology

Hi, I'm Brandon Carone.

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Human-centered music & audio AI researcher (PhD candidate in final year at NYU MARL), bridging music cognition and machine learning. Open to industry research roles starting May 2026.

About

My research integrates music cognition, machine learning, and audio-capable LLMs, with a focus on how we can build models whose perceptual abilities better reflect human music perception. By combining cognitive science, large-scale behavioral and streaming-log analyses, machine listening, and systematic benchmarking of audio models, I aim to understand how people discover, hear, and remember music, and to unveil when and why AI models' perceptual judgments for music align or diverge from those of humans. My ultimate goal is to build music AI systems whose internal representations and error patterns are interpretable in psychological terms and better aligned with human music perception.

Recently, I led the development of the MUSE Benchmark, a suite of music tasks for probing music perception and abstract, relational reasoning in audio-capable LLMs. This work has been submitted to ICASSP 2026 and forms the basis for ongoing evaluations of state-of-the-art audio models. I also led a LogicLM-style prompting study using a subset of the tasks and the stimuli from the MUSE benchmark. This work presented as a poster at NeurIPS in 2025, as an oral presentation at the AAAI 1st International Workshop on Emerging AI Technologies for Music, and will appear in the Proceedings of Machine Learning Research (PMLR). Previously, I developed SoundSignature, an interactive app that uses music information retrieval and a custom LLM to analyze listeners’ favorite tracks and provide personalized, educational feedback about their musical preferences.

Beyond academic work, I have industry-facing experience as a Research Scientist Intern at Deezer, where I investigate cognitively informed models of music discovery using large-scale streaming data, and as a consultant for EEG-based generative music applications. These projects reflect my broader goal: to bridge human-centered music cognition with modern AI systems in ways that are both scientifically grounded and practically impactful for music technology.

Programming: Advanced: Python, MATLAB, R | Intermediate: C++, JavaScript, HTML
Research: Machine Learning, Computational Modeling, GLMMs, Inferential Statistics
Audio and Music: Librosa, Essentia, MIRToolbox, madmom, mirdata, mir_eval, music21, mido
Frameworks: PyTorch, TensorFlow, Keras, Streamlit, Node.js
Tools & Technologies: Git, Logic Pro X, PsychoPy, JIRA

My current focus is on building benchmarks, models, and applications that close the gap between how humans and AI systems perceive music—making music technologies that are not only powerful, but also cognitively plausible and genuinely useful to listeners, creators, and researchers.

Education

New York University

New York, NY

Degree: Doctor of Philosophy (PhD) in Cognition and Perception
Expected Graduation: May 2026

Advisor: Professor Pablo Ripollès, Music and Audio Research Lab (MARL)
Fellowship: Dean’s Doctoral Fellowship
Relevant Coursework: Deep Learning, Computational Cognitive Modeling, Music Information Retrieval, Auditory Perception, Time Series Analysis

University of California, Los Angeles

Los Angeles, CA

Degree: Bachelor of Science (BS) in Cognitive Science
Graduation: June 2019

Specialization: Computing
Honors: Graduated with Honors
Thesis: Clinically studied or clinically proven? Memory for claims in print advertisements

Projects

            
              The MUSE Benchmark
            
              A benchmark for probing music perception and abstract, relational reasoning in humans and LLMs.
            
              Accomplishments
              
            Ran large-scale evaluations on both humans and models such as Gemini, Qwen, and Audio-Flamingo using zero-shot, few-shot, and chain-of-thought prompting paradigms.
Developed analysis scripts and GLMM-based statistical workflow to compare human and model performance across tasks and prompting strategies.
Tools: Python, R (lme4), HPC, audio-LLM APIs, GitHub

              LogicLM for Audio Models
            
              Evaluating logic-style prompting strategies to improve structured reasoning in audio-capable LLMs.
            
              Accomplishments
              
            Adapted LogicLM-style prompting to music perception tasks, comparing standard, chain-of-thought, and constrained reasoning prompts on the MUSE Benchmark.
Analyzed how different prompting schemes affect error types (e.g., rhythmic vs harmonic mistakes) and alignment with human judgments.
Built logging and evaluation pipelines to track per-task accuracy, confidence, and reasoning patterns across multiple models and seeds.
Tools: Python, JSON log parsing, Matplotlib, audio-LLM APIs, GitHub

SoundSignature
              Developed an app that integrates MIR with AI to analyze users' favorite songs.
            
            Accomplishments
            Developed SoundSignature, an interactive application that integrates MIR with AI to analyze users’ favorite songs and provide
personalized insights into their musical preferences
Tools: Python, Machine Learning, MIR, Natural Language Processing

Automated Jazz Chord Annotations
              Python tool for MIDI that labels jazz chords for dataset creation.
            
            Accomplishments
            Planning to use the tool to expand the CREMA Chord Recognition dataset with extended jazz chords.
Tools: Python, MIR, Music Analysis, Chord Recognition, MIDI

LSTM Neural Networks for fMRI Data
              Developed and optimized LSTM networks to analyze fMRI data.
            
            Accomplishments
            Used in NeuroMatch Academy to explore fMRI data and its applications in understanding brain activity patterns, such as classifying social vs. nonsocial interactions.
Tools: Python, PyTorch, Keras, Machine Learning, fMRI Analysis

Chord Similarity
              Modified CREMA model to explore alignment of human perception with the model's deep features.
            
            Accomplishments
            Implemented to assess how human perception of chord similarity aligns with the CREMA model's deep feature representations.
Collected and analyzed human similarity judgments on ii-V-I chord variations.
Tools: Python, Machine Learning, Tensorflow, Keras, Audio Analysis, Chord Recognition

Experience

Ripollés Lab, NYU

Graduate Student Researcher

Built the MUSE Benchmark, which is an experimental framework to assess music perception and auditory reasoning in multimodal LLMs using 10 different listening tasks that span melody, rhythm, harmony, and timbre, among others.
Evaluated four SoTA audio LLMs and compared their results with a large human sample (N=200). Across tasks, we found a persistent human–machine gap on abstract musical reasoning (especially in harmony and meter).
Exploring the neural mechanisms supporting music memory (i.e., what makes a song memorable?) using both behavioral and fMRI data. Using ratings of pleasure and a computational model of music novelty, we show that songs are better remembered when they are highly pleasurable to listen to and novel at the same time.

2021 – Present | New York, NY

Deezer, Paris

Research Scientist Intern

Investigated the cognitive mechanisms underlying music discovery by integrating large-scale streaming data analyses with frameworks from cognitive science.
Developed and formalized models linking familiarity, novelty, and cognitive effort to listener engagement and memory, framing discovery as a gradual process rather than an instantaneous event.
Implemented predictive and neural models that incorporate user embeddings, familiarity decay, and session-level cognitive load to estimate the likelihood of a song being liked.
Co-authored a paper (in preparation) proposing a cognitively informed framework for music recommender systems that operationalizes mental load, familiarity decay, and the dynamics of repeated exposure.

Jul 2025 – Oct 2025 | Paris, Île-de-France, France

NEXVOX Technologies

Consultant

Guiding improvements in software that reads signals from a consumer EEG headset and feeds them to an adaptive ML algorithm to create meditative music from real-time brain activity.
Advising on research strategy for clinical studies exploring the psychological benefits of adaptive music generation.

2023 – Present | Remote

Veterans Medical Research Foundation

Research Associate

Conducted research using neuroimaging (MRI, fMRI, DTI, ASL), audiology, eye-tracking, and neuropsychological assessments to evaluate cognition, perception, and brain health.

2019 – 2021 | San Diego, CA

Boutelle Lab, UCSD

Neuroimaging Operator

Operated structural MRI and multi-band functional MRI scans with children aged 8-12 to study reward sensitivity and obesity risk.
Processed neuroimaging data using HCP Pipelines, Freesurfer, FSL, and AFNI.
Conducted medical assessments and patient interviews for protocol management.

Sep 2020 – Jul 2021 | San Diego, CA

Music Mends Minds, Inc.

Manager / Executive Assistant

Led a team of five in supporting the Founder/Director with operations, fundraising, research, and legal tasks for a nonprofit creating musical support groups for patients with neurodegeneration.
Implemented the Public Education and Awareness Platform and launched the “Meet the Expert” Podcast, now with 19 episodes.
Designed the website and managed Google Ads campaigns, securing a $10,000/month in-kind ads grant.

2016 – 2021 | Los Angeles, CA

Memory and Cognition Lab, UCLA

Research Associate

Completed an honors thesis examining false memories for print advertisements with a sample of 500+ participants from the local community and Amazon Mechanical Turk.
Received grant to conduct an independent research study investigating the effects of listening to different musical genres on memory formation.
Coded the experiment and analyzed data using JASP.

2016 – 2019 | Los Angeles, CA

Publications

Peer-Reviewed Articles & Proceedings

Carone, B. J., Roman, I. R., & Ripollés, P. (2026). LLMs can read music, but struggle to hear it: An evaluation of core music perception tasks (In press). To be published in the Proceedings of Machine Learning Research. https://openreview.net/forum?id=hKE8tQzueC

Groves, K., Farbood, M. M., Carone, B. J., Ripollés, P., & Zuanazzi, A. (2025). Acoustic features of instrumental movie soundtracks elicit distinct and mostly non-overlapping extra-musical meanings in the mind of the listener. Scientific Reports, 15, 2327. https://doi.org/10.1038/s41598-025-86089-6

Carone, B. J., & Ripollés, P. (2024). SoundSignature: What Type of Music Do You Like? Proceedings of the IEEE 5th International Symposium on the Internet of Sounds (IS2).

Murphy, D. H., Schwartz, S. T., Alberts, K. O., Siegel, A. L. M., Carone, B. J., Castel, A. D., & Drolet, A. (2023). Clinically studied or clinically proven? Memory for claims in print advertisements. Applied Cognitive Psychology, 37(5), 1085–1093.

Workshop Papers

Carone, B. J., Roman, I. R., & Ripollés, P. (2025). Evaluating Multimodal Large Language Models on Core Music Perception Tasks. NeurIPS 2025 Workshop AI for Music: Where Creativity Meets Computation. https://arxiv.org/abs/2510.22455

Under Review

Carone, B. J., & Ripollés, P. (2025). The Effects of Novelty and Abstract Reward on Memory Performance.

Carone, B. J., Roman, I. R., & Ripollés, P. (2025). The MUSE Benchmark: Probing Music Perception and Auditory Relational Reasoning in Audio LLMs. https://arxiv.org/abs/2510.19055

In Preparation

Carone, B. J., Sguerra, B., Escobedo, G., Tamm, Y. M., & Bonnin, G. (2025). Discovery-Oriented Music Recommendation: The Role of Cognitive Effort and Familiarity.

Carone, B. J., Abrams, E. B., & Ripollés, P. (2025). The Neural Mechanisms of Novelty and Abstract Reward in Memory Performance.

Rodríguez-Vázquez, R., Carone, B. J., Groves, K., Namballa, R., Zuanazzi, A. R., & Ripollés, P. (2025). Identification of Basic Emotions Through Language Rhythms.

Talks & Media

Conference Presentations

Carone, B. J., Roman, I. R., & Ripollés, P. (2026). LLMs can read music, but struggle to hear it: An evaluation of core music perception tasks. Oral presentation at the AAAI 1st International Workshop on Emerging AI Technologies for Music. https://openreview.net/forum?id=hKE8tQzueC

Carone, B. J., Roman, I. R., & Ripollés, P. (2025). Evaluating Multimodal Large Language Models on Core Music Perception Tasks. Poster presented at the NeurIPS 2025 Workshop AI for Music: Where Creativity Meets Computation. Poster

Carone, B. J., & Ripollés, P. (2024, October). SoundSignature: What Type of Music Do You Like? Oral presentation at the 5th annual IEEE International Symposium on the Internet of Sounds (IS2 2024), International Audio Laboratories, Erlangen, Germany.

Carone, B. J., Abrams, E. B., & Ripollés, P. (2023, November). The Effects of Novelty and Abstract Reward on Memory Performance. Poster presented at the Society for Neuroscience, Washington, D.C.

Carone, B. J., Merritt, V. C., Jurick, S. M., & Jak, A. J. (2021, February). Effects of Major Depressive Disorder on Veterans. Poster presented at the International Neuropsychological Society, San Diego, CA.

Carone, B. J., Siegel, A. L. M., Castel, A. D., & Drolet, A. (2019, May). False memory for print advertisements. Poster presented at multiple conferences.

Invited Talks

Carone, B. J. (2025, October). The MUSE Benchmark: Probing Music Perception and Auditory Relational Reasoning in Audio LLMs. Invited speaker in Marcus Pearce’s Lab Meeting at the Queen Mary University of London.

Carone, B. J. (2025, October). The MUSE Benchmark: Probing Music Perception and Auditory Relational Reasoning in Audio LLMs. Guest Lecturer for the Artificial Intelligence course at the Queen Mary University of London.

Carone, B. J. (2025, October). The MUSE Benchmark: Probing Music Perception and Auditory Relational Reasoning in Audio LLMs. Invited speaker for the Machine Listening Group at the Queen Mary University of London.

Carone, B. J. (2025, September). Modeling Successful Music Discovery. Guest Lecturer at Deezer in Paris, France.

Carone, B. J. (2025, August). Do You Hear What I Hear? Music Perception in Minds and Machines. Guest Lecturer at Deezer in Paris, France.

Carone, B. J. (2024, July). SoundSignature: What Type of Music Do You Like? Guest Lecturer at the Deep Learning for Music Information Retrieval II: State-of-the-Art Algorithms Workshop at the Center for Computer Research in Music and Acoustics at Stanford University.

Carone, B. J. (2024, October). Music and AI: Theoretical and Practical Perspectives. Guest Lecturer at Queen Mary University of London.

Media

Carone, B. J., Zatorre, R. J. (Interviewees) & Zhu, X. (Host). (2022, October 6). Cognitive Neuroscience of Music and Memory [Audio podcast]. Research Journey Initiative.

Carone, B. J. (Interviewee) & Bowes, P. (Host). (2019, April 4). Why music helps us age better [Audio podcast]. Live Long and Master Aging Podcast.

Carone, B. J., Rosenstein, C. P. (Interviewees) & Sharp, R. (Producer). (2018, October 10). Music Mends Minds [Radio show]. BBC Radio 5 Live’s Up All Night with Rhod Sharp.