arXiv Machine Learning July 15, 2026 By Toru Nakashika, Kohei Yatabe

PolarBM: Complex-valued Boltzmann Machine for Modeling Audio Signals in Polar and Log-polar Coordinates

Read the original on arXiv Machine Learning →

arXiv:2607. 12417v1 Announce Type: new Abstract: Although vast amounts of data, such as audio signal spectra, are naturally represented using complex numbers, conventional machine learning methods often simplify complex-domain problems by employing frameworks designed for real-valued variables.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

1d ago

Encoding of musical structures in hidden units of restricted Boltzmann machines

arXiv:2509. 04899v4 Announce Type: replace-cross Abstract: Restricted Boltzmann machines (RBMs) are energy-based models originating from statistical physics, in which hidden units mediate the probability distribution of high-dimensional visible configurations.

By Mutsumi Kobayashi, Hiroshi Watanabe

arXiv Machine Learning

Jun 24

NeuroSonic: Conditional Flow Matching for EEG-to-Speech Reconstruction

arXiv:2606. 24087v1 Announce Type: new Abstract: Reconstructing continuous speech from scalp electroencephalography (EEG) remains fundamentally challenging.

By Wenhao Gao, Yifan Wang, Yijia Ma, Carl Yang, Wen Li, Chenyu You

llms diffusion benchmarks

arXiv AI

Jun 10

Whisper-GPT -- Continuous Discrete Hybrid Representation Language Models For Speech And Music

arXiv:2412. 11449v2 Announce Type: replace-cross Abstract: We propose WHISPER-GPT: A generative large language model (LLM) for speech and music that allows us to work with continuous audio representations and discrete tokens simultaneously as part of a single architecture.

By Prateek Verma

llms multimodal

arXiv AI

Aug 12

The Gaussian-Multinoulli Restricted Boltzmann Machine: A Potts Model Extension of the GRBM

arXiv:2505. 11635v2 Announce Type: cross Abstract: Many real-world tasks, from associative memory to symbolic reasoning, benefit from discrete, structured representations that standard continuous latent models can struggle to express.

By Nikhil Kapasi, Mohamed Elfouly, William Whitehead, Luke Theogarajan

benchmarks

arXiv AI

Jul 14

Encoder-Side Neuron Identification and Amplification for Acoustic Perception in Large Audio-Language Models

arXiv:2607. 11801v1 Announce Type: cross Abstract: Large audio-language models (LALMs) often underperform on fine-grained, non-semantic attributes of speech, such as a speaker's emotion, despite strong performance on speech content.

By Yu-Han Huang, Chih-Kai Yang, Ke-Han Lu, An-Yu Cheng, Hung-yi Lee

llms fine-tuning

arXiv AI

Jun 4

The Differentiable Auditory Loop (DAL): An ML Framework for Hyper-Personalized Hearing Aids

arXiv:2606. 04103v1 Announce Type: cross Abstract: Conventional hearing aids rely on fixed, frequency-dependent amplification and compression to manage reduced sensitivity, which often fails to provide sufficient listening support in complex environments, such as situations with multiple speakers (the ``cocktail party'' problem).

By Alejandro Ballesta Rosen, Jason Mikiel-Hunter, Julian Maclaren, Jack Collins, Richard F. Lyon, Simon Carlile

fine-tuning

Related stories

Encoding of musical structures in hidden units of restricted Boltzmann machines

NeuroSonic: Conditional Flow Matching for EEG-to-Speech Reconstruction

Whisper-GPT -- Continuous Discrete Hybrid Representation Language Models For Speech And Music

The Gaussian-Multinoulli Restricted Boltzmann Machine: A Potts Model Extension of the GRBM

Encoder-Side Neuron Identification and Amplification for Acoustic Perception in Large Audio-Language Models

The Differentiable Auditory Loop (DAL): An ML Framework for Hyper-Personalized Hearing Aids