Tootfinder

#Drumpf shakedown of #UCLA over alleged "Anti-Semitism" on campus sees #USGov freeze over $500 million in federal funding for imperiled research programs and demands school pay a

Zelfs de T geeft regeringsdeelname van de PVV op.
(Via @… )

neuzugang in unseren wintermappen: "carol of the bells" in einer beeindruckenden orchesterfassung. #bva #2025
https://de.m.wikipedia.org/wiki/Schtschedryk

A teardown of the $800 Meta Ray-Ban Display reveals a system of mirrors enabling its "geometric" waveguide tech, setting it apart from other AR glasses (Elizabeth Chamberlain/iFixit News)
https://www.ifixit.com/News/113543/theres-

There’s Groundbreaking Waveguide Tech Inside Meta’s $800 AR Glasses But Don’t Count on Fixing Them
Imagine an AR repair guide that follows along with your repair. We’re excited by the possibility, but you won’t be able to fix these glasses just yet.

mist: MIST protein interaction database (2020)
The Molecular Interaction Search Tool (MIST) is a comprehensive resource of molecular interactions, assembled from severla primary sources. MIST currently supports several species, including:.
This network has 27105 nodes and 334495 edges.
Tags: Biological, Protein interactions, Unweighted

#MontereyCarWeek they run the track backwards at #LagunaSeca to turn The Corkscrew into a hill climb. There’s a live stream running on YouTube but no action just yet.

⚠️Das Osnabrücker Paradoxon: Eine Analyse des blockierten Radentscheids und der Politik der #Verkehrswende
Zitat:
Die am häufigsten genannte Begründung ist „Platzmangel“. Dies ist keine Nebensächlichkeit, sondern der Kern des Problems: Die Stadt weigert sich, den Raum vom Autoverkehr umzuverteilen, was die eigentliche Forderung des Radentscheids war.
👇🏻

Trump's One Big Beautiful Bill Act mandates all new US border surveillance towers are "autonomous", effectively granting Anduril a monopoly on new contracts (Sam Biddle/The Intercept)
https://theintercept.com/2025/07/09/trump-big-beautiful-bill-…

Trump’s Big Beautiful Gift to Anduril
The One Big Beautiful Bill Act requires all new border surveillance towers to be certified "autonomous." Only Anduril's fit the bill.

Warstwa kurzu na laptopie jako znak, że już czas robić księgowość.

Simulation of one and two qubit superconducting quantum gates under the non-Markovian $1/f$ noise
Yinjia Chen, Shuocang Zhang, Qiang Shi
https://arxiv.org/abs/2509.07693 https:/…

Simulation of one and two qubit superconducting quantum gates under the non-Markovian $1/f$ noise
Non-Markovian $1/f$ noise consists a dominant source of decoherence in superconducting qubits, yet its slow nature poses a significant challenge for accurate simulation. Here we develop a hierarchical equations of motion (HEOM) framework that enables efficient and reliable modeling of qubit dynamics and gate operations under $1/f$ noise. By using the approach, it is first shown that perturbative quantum master equations may fail to reproduce the correct dephasing dynamics of a qubit coupled to …

florentine_families: Padgett Florentine families
Multiplex network with 2 edge types representing marriage alliances and business relationships between Florentine families during the Italian Renaissance. Data hosted by Manlio De Domenico.
This network has 16 nodes and 35 edges.
Tags: Social, Relationships, Multilayer, Unweighted
…

#CarsAndCoffee starting off #MontereyCarWeek with our usual gathering at Seaside. My son brought his (recently acquired) 2019 GT3 here for the week, and I drove his matching guards red 1990 C4 that he’s owned for 18years now (same spec as the one on the cover of the Sun perf book I wro…

Electron-positron scattering at finite temperature in Podolsky electrodynamics
D. S. Cabral, L. A. S. Evangelista, L. H. A. R. Ferreira, A. F. Santos
https://arxiv.org/abs/2509.06568

Electron-positron scattering at finite temperature in Podolsky electrodynamics
The electron-positron annihilation process is investigated within the framework of Podolsky's generalized electrodynamics at finite temperature. In this theory, a higher-derivative term modifies the photonic kinetic sector, introducing a massive mode while preserving gauge invariance. Thermal effects are incorporated using the real-time Thermo Field Dynamics formalism. The total cross section is calculated, and the individual contributions of the Podolsky parameter and thermal effects are analy…

Retentive Relevance: Capturing Long-Term User Value in Recommendation Systems
Saeideh Bakhshi, Phuong Mai Nguyen, Robert Schiller, Tiantian Xu, Pawan Kodandapani, Andrew Levine, Cayman Simpson, Qifan Wang
https://arxiv.org/abs/2510.07621

Retentive Relevance: Capturing Long-Term User Value in Recommendation Systems
Recommendation systems have traditionally relied on short-term engagement signals, such as clicks and likes, to personalize content. However, these signals are often noisy, sparse, and insufficient for capturing long-term user satisfaction and retention. We introduce Retentive Relevance, a novel content-level survey-based feedback measure that directly assesses users' intent to return to the platform for similar content. Unlike other survey measures that focus on immediate satisfaction, Retenti…

Gibt es hier Menschen, die die extrem unterschiedlichen Wassertemperaturen im französischen Mittelmeer erklären können?
https://www.wassertemperatur.org/frankreich/

Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- LaunchpadGPT: Language Model as Music Visualization Designer on Launchpad
Siting Xu, Yolo Yunlong Tang, Feng Zheng

Nuclear and electron scattering by neutrinos and dark matter in condensed systems
James B. Dent, Barry A. Friedman, Jayden L. Newstead, Subir Sabharwal
https://arxiv.org/abs/2510.06574

Nuclear and electron scattering by neutrinos and dark matter in condensed systems
Low-threshold dark matter detectors, in particular cryogenic detectors based on dielectric materials, are among the best tools for probing sub-GeV dark matter masses. In the coming years detectors of this type will become sensitive to solar neutrino scattering. Previous work has shown that, for dark matter scattering at very low recoil energies, one must include collective excitations of the electrons in the solid. In this work, we have computed the collective excitations due to neutrino scatte…

Hervorragend!
https://taz.de/Wolfram-Weimers-Gender-Verbot/!6101942/

Replaced article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Loschmidt echo, emerging dual unitarity and scaling of generalized temporal entropies after quenc...
Stefano Carignano, Luca Tagliacozzo

Mogłoby się wydawać, że nie jedziemy, pomimo upływu pory odjazdu i zniknięcia naszego pociągu z tablicy odjazdów. Nie dajmy się jednak oszukać pozorom — to jedynie kwestia przyjęcia niewłaściwego układu odniesienia. Z punktu odniesienia odjeżdżającego z sąsiedniego peronu pociągu, to my się poruszamy.

Density matrix analysis of systems influenced by periodic Hamiltonians
Soham Sen, Manjari Dutta, Sunandan Gangopadhyay
https://arxiv.org/abs/2507.06355 htt…

Density matrix analysis of systems influenced by periodic Hamiltonians
In this work, we consider simple systems that are influenced by Hamiltonians with time periodicity. Our analysis is mainly focussed on the density matrix approach and aims to solve the Liouville equation of motion from which one can extract the state of the system when the system is in a pure state. We start our analysis with the standard Rabi-oscillation problem. We consider a density matrix corresponding to the entire model system and solve the Liouville equation of motion. We have then made …

Crosslisted article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- Moises-Light: Resource-efficient Band-split U-Net For Music Source Separation
Yun-Ning (Amy), Hung, Igor Pereira, Filip Korzeniowski

Crosslisted article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Energy and information: a chronicle of hesitations on the role of the observer in physics
Didier Lairez

Uber and Shanghai-based robotaxi company Momenta plan to test Level 4 fully driverless cars in Munich from 2026, as a precursor to launching a robotaxi service (Andrew J. Hawkins/The Verge)
https://www.theverge.com/news/772639/uber-momenta-l4-driverless-t…

Uber and Momenta will test fully driverless cars in Germany
Uber and Momenta will test fully driverless, level 4 vehicles in Munich, Germany, in 2026. The companies also plan on launching a robotaxi service.

Symbol odwiecznych zmagań Polaków z #ZUS-em.
https://www.openstreetmap.org/?mlat=51.19686&mlon=16.15153#map=16/51.19686/16.15153

marvel_universe: Marvel Universe social network
The Marvel Universe collaboration network, where two Marvel characters are considered linked if they jointly appear in the same Marvel comic book.
This network has 19428 nodes and 95497 edges.
Tags: Social, Fictional, Unweighted
https://networks.skewed.de/ne…

We have about 5000 miles on our Polestar 3 now, and like it a lot, especially as an upgrade from a Tesla. Its controls and interior are Tesla-like to drive (one big screen) but with head-up display, much better build quality, very quiet, one of the best sound systems (by B&W) available in any car. It competes more with the Model X than the smaller Y. The software issues that bugged us in the early days were fixed with 1.3.x upgrade last June. Also it looks cool…

Dysonans poznawczy: piekarnia - cukiernia Okoń.

AudioMarathon: A Comprehensive Benchmark for Long-Context Audio Understanding and Efficiency in Audio LLMs
Peize He, Zichen Wen, Yubo Wang, Yuxuan Wang, Xiaoqian Liu, Jiajie Huang, Zehui Lei, Zhuangcheng Gu, Xiangqi Jin, Jiabing Yang, Kai Li, Zhifei Liu, Weijia Li, Cunxiang Wang, Conghui He, Linfeng Zhang
https://arxiv.org/abs/2510.07293…

AudioMarathon: A Comprehensive Benchmark for Long-Context Audio Understanding and Efficiency in Audio LLMs
Processing long-form audio is a major challenge for Large Audio Language models (LALMs). These models struggle with the quadratic cost of attention ($O(N^2)$) and with modeling long-range temporal dependencies. Existing audio benchmarks are built mostly from short clips and do not evaluate models in realistic long context settings. To address this gap, we introduce AudioMarathon, a benchmark designed to evaluate both understanding and inference efficiency on long-form audio. AudioMarathon provi…

Exact bound of power-efficiency trade-off in finite-time thermodynamic cycles
R. X. Zhai, Xin Yue, C. P. Sun
https://arxiv.org/abs/2509.07721 https://arxiv…

Exact bound of power-efficiency trade-off in finite-time thermodynamic cycles
Power and efficiency are fundamental criteria for evaluating the performance of thermodynamic cycles. However, it is generally impossible to maximize both simultaneously. In particular, achieving maximum efficiency inevitably leads to vanishing power as the cycle duration approaches infinity. A quantitative characterization of this trade-off yields significant theoretical and practical implications. In this letter, we analytically derive an exact bound constraining power and efficiency in low-d…

yahoo_song: Yahoo song ratings (2011)
A bipartite network of users and songs they rated, as used in the 2011 KDD Cup and extracted from Yahoo! Music. Edge weights denote a rating scaled from 0 to 100. More information about this data set is available at http://konect.cc/networks/yahoo-song.
Th…

XLSR-Kanformer: A KAN-Intergrated model for Synthetic Speech Detection
Phuong Tuan Dat, Tran Huy Dat
https://arxiv.org/abs/2510.06706 https://arxiv.org/pdf…

XLSR-Kanformer: A KAN-Intergrated model for Synthetic Speech Detection
Recent advancements in speech synthesis technologies have led to increasingly sophisticated spoofing attacks, posing significant challenges for automatic speaker verification systems. While systems based on self-supervised learning (SSL) models, particularly the XLSR-Conformer architecture, have demonstrated remarkable performance in synthetic speech detection, there remains room for architectural improvements. In this paper, we propose a novel approach that replaces the traditional Multi-Layer…

us_roads: United States roads (2000)
The road networks of the 50 US States and the District of Columbia based on UA Census 2000 TIGER/Line Files. Edges are stretches of road and vertices are intersections of roads. The data sets were assembled by Dominik Schultes. The 'merged' network contains all the states merged together.
This network has 540981 nodes and 664215 edges.
Tags: Transportation, Roads, Unweighted

Building causation links in stochastic nonlinear systems from data
Sergio Chibbaro, Cyril Furtlehner, Th\'eo Marchetta, Andrei-Tiberiu Pantea, Davide Rossetti
https://arxiv.org/abs/2509.07701

Building causation links in stochastic nonlinear systems from data
Causal relationships play a fundamental role in understanding the world around us. The ability to identify and understand cause-effect relationships is critical to making informed decisions, predicting outcomes, and developing effective strategies. However, deciphering causal relationships from observational data is a difficult task, as correlations alone may not provide definitive evidence of causality. In recent years, the field of machine learning (ML) has emerged as a powerful tool, offerin…

Pitch Estimation With Mean Averaging Smoothed Product Spectrum And Musical Consonance Evaluation Using MASP
Murat Yasar Baskin
https://arxiv.org/abs/2510.06625 https://

Pitch Estimation With Mean Averaging Smoothed Product Spectrum And Musical Consonance Evaluation Using MASP
This study introduces Mean Averaging Smoothed Product (MASP) Spectrum, which is a modified version of the Harmonic Product Spectrum, designed to enhance pitch estimation for many algorithm-wise deceptive frequency spectra that still lead clear pitches, for both harmonic and inharmonic cases. By introducing a global mean based smoothing for spectrum, the MASP algorithm diminishes the unwanted sensitivity of HPS for spectra with missing partials. The method exhibited robust pitch estimations cons…

Oto kolorowy przykład ze słownika gwary poznańskiej:
"""
Miałem zawsze książkę w gołębniku. Zamiast krowy chędożyć siadałem przy drzwiach i czytałem. Miałem w tym czasie krowy chędożyć.
"""
#TIL, że "chędożyć" to też znaczy "czyścić, porządkować". I od razu się wiele w Sapkowskim wyjaśniło.

Spinning mixtures: nonreciprocity transfers chirality across scales in scalar densities
Giulia Pisegna, Suropriya Saha
https://arxiv.org/abs/2509.07630 https://

Spinning mixtures: nonreciprocity transfers chirality across scales in scalar densities
We present a minimal model for mixtures of chiral active particles, formulated solely in terms of the number densities of the species. Nonreciprocity and chirality enter the chemical potential at vanishing order in spatial gradients, taking the form of local and non-local contributions. Chirality manifests as the curl of the nonreciprocal current, while the latter is simultaneously strongly modified by former. Their interplay generates distinctive dynamical states, including phase separation wi…

Benchmarking Fake Voice Detection in the Fake Voice Generation Arms Race
Xutao Mao, Ke Li, Cameron Baird, Ezra Xuanru Tao, Dan Lin
https://arxiv.org/abs/2510.06544 https://

Benchmarking Fake Voice Detection in the Fake Voice Generation Arms Race
As advances in synthetic voice generation accelerate, an increasing variety of fake voice generators have emerged, producing audio that is often indistinguishable from real human speech. This evolution poses new and serious threats across sectors where audio recordings serve as critical evidence. Although fake voice detectors are also advancing, the arms race between fake voice generation and detection has become more intense and complex. In this work, we present the first large-scale, cross-do…

Piargi ze śliwkami
#SłowaNaOpak

Controlling the Glass Transition through Active Fluctuating Interactions
Emir Sezik, Henry Alston, Thibault Bertrand
https://arxiv.org/abs/2509.07619 https://

Controlling the Glass Transition through Active Fluctuating Interactions
Fluctuating pairwise interactions are understood to drive fluid-like states in dense biological systems. These states find a broad range of functionalities, such as directing growth during morphogenesis and forming aggregates with heightened mechanical response. However, a tractable model capturing the role of microscopic fluctuating interactions in these structural transitions is crucially lacking. Here, we study a $p$-spin model with fluctuating pairwise couplings (of strength $D_a$ and persi…

BACHI: Boundary-Aware Symbolic Chord Recognition Through Masked Iterative Decoding on Pop and Classical Music
Mingyang Yao, Ke Chen, Shlomo Dubnov, Taylor Berg-Kirkpatrick
https://arxiv.org/abs/2510.06528

BACHI: Boundary-Aware Symbolic Chord Recognition Through Masked Iterative Decoding on Pop and Classical Music
Automatic chord recognition (ACR) via deep learning models has gradually achieved promising recognition accuracy, yet two key challenges remain. First, prior work has primarily focused on audio-domain ACR, while symbolic music (e.g., score) ACR has received limited attention due to data scarcity. Second, existing methods still overlook strategies that are aligned with human music analytical practices. To address these challenges, we make two contributions: (1) we introduce POP909-CL, an enhance…

Emergence of continuously varying critical exponents in coupled map lattice as an effect of quenched disorder
Priyanka D. Bhoyar, Govindan Rangarajan, Prashant M. gade
https://arxiv.org/abs/2509.07529 …

Emergence of continuously varying critical exponents in coupled map lattice as an effect of quenched disorder
The transition to an absorbing phase in a spatiotemporal system is a well-investigated nonequilibrium dynamic transition. The absorbing phase transitions fall into a few universality classes, defined by the critical exponents observed at the critical point. We present a coupled map lattice (CML) model with quenched disorder in the couplings. In this model, spatial disorders are introduced in the form of asymmetric coupling with a larger coupling ($p$) to a neighbor on the right and a smaller co…

[2025-10-09 Thu (UTC), 5 new articles found for cs.SD Sound]
toXiv_bot_toot

Quantum Fisher Information as a Measure of Symmetry Breaking in Quantum Many-Body Systems
Shion Yamashika, Shimpei Endo, Hiroyasu Tajima
https://arxiv.org/abs/2509.07468 https:/…

Quantum Fisher Information as a Measure of Symmetry Breaking in Quantum Many-Body Systems
Symmetry breaking underlies diverse phenomena from phase transitions in condensed matter to fundamental interactions in gauge theories. Despite many proposed indicators, a general quantification of symmetry breaking that is faithful, computable, and valid in the thermodynamic limit has remained elusive. Here, within quantum resource theory, we propose the quantum Fisher information (QFI) as such a measure. We demonstrate its utility by computing QFI for paradigmatic models: in the BCS supercond…

Crosslisted article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- Audio-Visual Separation with Hierarchical Fusion and Representation Alignment
Han Hu, Dongheng Lin, Qiming Huang, Yuqi Hou, Hyung Jin Chang, Jianbo Jiao

A set of master variables for the two-star random graph
Pawat Akara-pipattana, Oleg Evnin
https://arxiv.org/abs/2509.07423 https://arxiv.org/pdf/2509.07423…

A set of master variables for the two-star random graph
The two-star random graph is the simplest exponential random graph model with nontrivial interactions between the graph edges. We propose a set of auxiliary variables that control the thermodynamic limit where the number of vertices N tends to infinity. Such 'master variables' are usually highly desirable in treatments of 'large N' statistical field theory problems. For the dense regime when a finite fraction of all possible edges are filled, this construction recovers the mean field solution o…

Leveraging Whisper Embeddings for Audio-based Lyrics Matching
Eleonora Mancini, Joan Serr\`a, Paolo Torroni, Yuki Mitsufuji
https://arxiv.org/abs/2510.08176 https://

Leveraging Whisper Embeddings for Audio-based Lyrics Matching
Audio-based lyrics matching can be an appealing alternative to other content-based retrieval approaches, but existing methods often suffer from limited reproducibility and inconsistent baselines. In this work, we introduce WEALY, a fully reproducible pipeline that leverages Whisper decoder embeddings for lyrics matching tasks. WEALY establishes robust and transparent baselines, while also exploring multimodal extensions that integrate textual and acoustic features. Through extensive experiments…

Bayesian Prior Construction for Uncertainty Quantification in First-Principles Statistical Mechanics
Derick E. Ober, Sesha Sai Behara, Anton Van der Ven
https://arxiv.org/abs/2509.07326

Bayesian Prior Construction for Uncertainty Quantification in First-Principles Statistical Mechanics
First-principles statistical mechanics enables the prediction of thermodynamic and kinetic properties of materials, but is computationally expensive. Many approaches require surrogate models to calculate energies within Monte Carlo or molecular dynamics simulations. Inexpensive surrogates such as cluster expansions enable otherwise intractable calculations by interpolating data from higher accuracy methods, such as Density Functional Theory (DFT). Surrogate models introduce uncertainty into dow…

Detecting and Mitigating Insertion Hallucination in Video-to-Audio Generation
Liyang Chen, Hongkai Chen, Yujun Cai, Sifan Li, Qingwen Ye, Yiwei Wang
https://arxiv.org/abs/2510.08078

Detecting and Mitigating Insertion Hallucination in Video-to-Audio Generation
Video-to-Audio generation has made remarkable strides in automatically synthesizing sound for video. However, existing evaluation metrics, which focus on semantic and temporal alignment, overlook a critical failure mode: models often generate acoustic events, particularly speech and music, that have no corresponding visual source. We term this phenomenon Insertion Hallucination and identify it as a systemic risk driven by dataset biases, such as the prevalence of off-screen sounds, that remains…

Thermodynamic Irreversibility in Underdamped Brownian Motion with Spatial Temperature Gradients
Mesfin Taye
https://arxiv.org/abs/2509.07272 https://arxiv.…

Thermodynamic Irreversibility in Underdamped Brownian Motion with Spatial Temperature Gradients
We analyze underdamped Brownian motion in non-isothermal media with quadratic, linear, and piecewise-constant temperature profiles. Exact identities for entropy production and entropy extraction are derived, addressing whether a vanishing rate implies equilibrium. For a free particle (no external forces or ratchets), the instantaneous entropy production and extraction rates decay to zero at long times despite a spatial temperature gradient. However, their time-integrated values remain finite, d…

Attribution-by-design: Ensuring Inference-Time Provenance in Generative Music Systems
Fabio Morreale, Wiebke Hutiri, Joan Serr\`a, Alice Xiang, Yuki Mitsufuji
https://arxiv.org/abs/2510.08062

Attribution-by-design: Ensuring Inference-Time Provenance in Generative Music Systems
The rise of AI-generated music is diluting royalty pools and revealing structural flaws in existing remuneration frameworks, challenging the well-established artist compensation systems in the music industry. Existing compensation solutions, such as piecemeal licensing agreements, lack scalability and technical rigour, while current data attribution mechanisms provide only uncertain estimates and are rarely implemented in practice. This paper introduces a framework for a generative music infras…

Exact large deviations and emergent long-range correlations in sequential quantum East circuits
Jimin Li, Bruno Bertini, Juan P. Garrahan, Robert L. Jack
https://arxiv.org/abs/2509.07182

Exact large deviations and emergent long-range correlations in sequential quantum East circuits
Exploiting quantum measurements is a promising route for preparation of correlated quantum states. We use methods from large deviation theory to solve this problem exactly for a specific system: the deterministic quantum East circuit with boundary measurements. We show that conditioning on measurement outcomes generates a long-range correlated state, despite typical trajectories being trivial. We derive the channel that optimally realizes the rare measurement trajectories, and establish a forma…

Personality-Enhanced Multimodal Depression Detection in the Elderly
Honghong Wang, Jing Deng, Rong Zheng
https://arxiv.org/abs/2510.08004 https://arxiv.org…

Personality-Enhanced Multimodal Depression Detection in the Elderly
This paper presents our solution to the Multimodal Personality-aware Depression Detection (MPDD) challenge at ACM MM 2025. We propose a multimodal depression detection model in the Elderly that incorporates personality characteristics. We introduce a multi-feature fusion approach based on a co-attention mechanism to effectively integrate LLDs, MFCCs, and Wav2Vec features in the audio modality. For the video modality, we combine representations extracted from OpenFace, ResNet, and DenseNet to co…

The role of the density of states in Bose-Einstein condensation
Alexios P. Polychronakos, Stephane Ouvry
https://arxiv.org/abs/2509.07089 https://arxiv.org…

The role of the density of states in Bose-Einstein condensation
The onset of Bose-Einstein condensation in systems with { various} densities of states is examined, with particular attention to the role of the behavior of their {energy} spectrum at low and high energies. Specifically, the results of Chatterjee and Diaconis, which rely exclusively on the high-energy behavior, are compared and reconciled with those of the standard physics approach, where the existence of condensation is determined by the low-energy behavior.

IntMeanFlow: Few-step Speech Generation with Integral Velocity Distillation
Wei Wang, Rong Cao, Yi Guo, Zhengyang Chen, Kuan Chen, Yuanyuan Huo
https://arxiv.org/abs/2510.07979 …

IntMeanFlow: Few-step Speech Generation with Integral Velocity Distillation
Flow-based generative models have greatly improved text-to-speech (TTS) synthesis quality, but inference speed remains limited by the iterative sampling process and multiple function evaluations (NFE). The recent MeanFlow model accelerates generation by modeling average velocity instead of instantaneous velocity. However, its direct application to TTS encounters challenges, including GPU memory overhead from Jacobian-vector products (JVP) and training instability due to self-bootstrap processes…

[2025-09-10 Wed (UTC), 11 new articles found for cond-mat.stat-mech Statistical Mechanics]
toXiv_bot_toot

ACMID: Automatic Curation of Musical Instrument Dataset for 7-Stem Music Source Separation
Ji Yu, Yang shuo, Xu Yuetonghui, Liu Mengmei, Ji Qiang, Han Zerui
https://arxiv.org/abs/2510.07840

ACMID: Automatic Curation of Musical Instrument Dataset for 7-Stem Music Source Separation
Most current music source separation (MSS) methods rely on supervised learning, limited by training data quan- tity and quality. Though web-crawling can bring abundant data, platform-level track labeling often causes metadata mismatches, impeding accurate "audio-label" pair acquisi- tion. To address this, we present ACMID: a dataset for MSS generated through web crawling of extensive raw data, fol- lowed by automatic cleaning via an instrument classifier built on a pre-trained audio encoder tha…

Replaced article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Open boundary conditions of the $D^{(2)}_3$ spin chain and sectors of conformal field theories
Pete Rigas

INFER : Learning Implicit Neural Frequency Response Fields for Confined Car Cabin
Harshvardhan C. Takawale, Nirupam Roy, Phil Brown
https://arxiv.org/abs/2510.07442 https://

INFER : Learning Implicit Neural Frequency Response Fields for Confined Car Cabin
Accurate modeling of spatial acoustics is critical for immersive and intelligible audio in confined, resonant environments such as car cabins. Current tuning methods are manual, hardware-intensive, and static, failing to account for frequency selective behaviors and dynamic changes like passenger presence or seat adjustments. To address this issue, we propose INFER: Implicit Neural Frequency Response fields, a frequency-domain neural framework that is jointly conditioned on source and receiver …

[2025-10-10 Fri (UTC), 7 new articles found for cs.SD Sound]
toXiv_bot_toot

Spectral and Rhythm Feature Performance Evaluation for Category and Class Level Audio Classification with Deep Convolutional Neural Networks
Friedrich Wolf-Monheim
https://arxiv.org/abs/2509.07756

Spectral and Rhythm Feature Performance Evaluation for Category and Class Level Audio Classification with Deep Convolutional Neural Networks
Next to decision tree and k-nearest neighbours algorithms deep convolutional neural networks (CNNs) are widely used to classify audio data in many domains like music, speech or environmental sounds. To train a specific CNN various spectral and rhythm features like mel-scaled spectrograms, mel-frequency cepstral coefficients (MFCC), cyclic tempograms, short-time Fourier transform (STFT) chromagrams, constant-Q transform (CQT) chromagrams and chroma energy normalized statistics (CENS) chromagrams…

Spectral Masking and Interpolation Attack (SMIA): A Black-box Adversarial Attack against Voice Authentication and Anti-Spoofing Systems
Kamel Kamel, Hridoy Sankar Dutta, Keshav Sood, Sunil Aryal
https://arxiv.org/abs/2509.07677

Spectral Masking and Interpolation Attack (SMIA): A Black-box Adversarial Attack against Voice Authentication and Anti-Spoofing Systems
Voice Authentication Systems (VAS) use unique vocal characteristics for verification. They are increasingly integrated into high-security sectors such as banking and healthcare. Despite their improvements using deep learning, they face severe vulnerabilities from sophisticated threats like deepfakes and adversarial attacks. The emergence of realistic voice cloning complicates detection, as systems struggle to distinguish authentic from synthetic audio. While anti-spoofing countermeasures (CMs) …

Neural Proxies for Sound Synthesizers: Learning Perceptually Informed Preset Representations
Paolo Combes, Stefan Weinzierl, Klaus Obermayer
https://arxiv.org/abs/2509.07635 htt…

Neural Proxies for Sound Synthesizers: Learning Perceptually Informed Preset Representations
Deep learning appears as an appealing solution for Automatic Synthesizer Programming (ASP), which aims to assist musicians and sound designers in programming sound synthesizers. However, integrating software synthesizers into training pipelines is challenging due to their potential non-differentiability. This work tackles this challenge by introducing a method to approximate arbitrary synthesizers. Specifically, we train a neural network to map synthesizer presets onto an audio embedding space …

Competitive Audio-Language Models with Data-Efficient Single-Stage Training on Public Data
Gokul Karthik Kumar, Rishabh Saraf, Ludovick Lepauloux, Abdul Muneer, Billel Mokeddem, Hakim Hacid
https://arxiv.org/abs/2509.07526

Competitive Audio-Language Models with Data-Efficient Single-Stage Training on Public Data
Large language models (LLMs) have transformed NLP, yet their integration with audio remains underexplored -- despite audio's centrality to human communication. We introduce Falcon3-Audio, a family of Audio-Language Models (ALMs) built on instruction-tuned LLMs and Whisper encoders. Using a remarkably small amount of public audio data -- less than 30K hours (5K unique) -- Falcon3-Audio-7B matches the best reported performance among open-weight models on the MMAU benchmark, with a score of 64.14,…

Target matching based generative model for speech enhancement
Taihui Wang, Rilin Chen, Tong Lei, Andong Li, Jinzheng Zhao, Meng Yu, Dong Yu
https://arxiv.org/abs/2509.07521 http…

Target matching based generative model for speech enhancement
The design of mean and variance schedules for the perturbed signal is a fundamental challenge in generative models. While score-based and Schrödinger bridge-based models require careful selection of the stochastic differential equation to derive the corresponding schedules, flow-based models address this issue via vector field matching. However, this strategy often leads to hallucination artifacts and inefficient training and inference processes due to the potential inclusion of stochastic com…

Progressive Facial Granularity Aggregation with Bilateral Attribute-based Enhancement for Face-to-Speech Synthesis
Yejin Jeon, Youngjae Kim, Jihyun Lee, Hyounghun Kim, Gary Geunbae Lee
https://arxiv.org/abs/2509.07376

Progressive Facial Granularity Aggregation with Bilateral Attribute-based Enhancement for Face-to-Speech Synthesis
For individuals who have experienced traumatic events such as strokes, speech may no longer be a viable means of communication. While text-to-speech (TTS) can be used as a communication aid since it generates synthetic speech, it fails to preserve the user's own voice. As such, face-to-voice (FTV) synthesis, which derives corresponding voices from facial images, provides a promising alternative. However, existing methods rely on pre-trained visual encoders, and finetune them to align with speec…

When Fine-Tuning is Not Enough: Lessons from HSAD on Hybrid and Adversarial Audio Spoof Detection
Bin Hu, Kunyang Huang, Daehan Kwak, Meng Xu, Kuan Huang
https://arxiv.org/abs/2509.07323

When Fine-Tuning is Not Enough: Lessons from HSAD on Hybrid and Adversarial Audio Spoof Detection
The rapid advancement of AI has enabled highly realistic speech synthesis and voice cloning, posing serious risks to voice authentication, smart assistants, and telecom security. While most prior work frames spoof detection as a binary task, real-world attacks often involve hybrid utterances that mix genuine and synthetic speech, making detection substantially more challenging. To address this gap, we introduce the Hybrid Spoofed Audio Dataset (HSAD), a benchmark containing 1,248 clean and 41,0…

Adversarial Attacks on Audio Deepfake Detection: A Benchmark and Comparative Study
Kutub Uddin, Muhammad Umar Farooq, Awais Khan, Khalid Mahmood Malik
https://arxiv.org/abs/2509.07132

Adversarial Attacks on Audio Deepfake Detection: A Benchmark and Comparative Study
The widespread use of generative AI has shown remarkable success in producing highly realistic deepfakes, posing a serious threat to various voice biometric applications, including speaker verification, voice biometrics, audio conferencing, and criminal investigations. To counteract this, several state-of-the-art (SoTA) audio deepfake detection (ADD) methods have been proposed to identify generative AI signatures to distinguish between real and deepfake audio. However, the effectiveness of thes…

End-to-End Efficiency in Keyword Spotting: A System-Level Approach for Embedded Microcontrollers
Pietro Bartoli, Tommaso Bondini, Christian Veronesi, Andrea Giudici, Niccol\`o Antonello, Franco Zappa
https://arxiv.org/abs/2509.07051

End-to-End Efficiency in Keyword Spotting: A System-Level Approach for Embedded Microcontrollers
Keyword spotting (KWS) is a key enabling technology for hands-free interaction in embedded and IoT devices, where stringent memory and energy constraints challenge the deployment of AI-enabeld devices. In this work, we systematically evaluate and compare several state-of-the-art lightweight neural network architectures, including DS-CNN, LiCoNet, and TENet, alongside our proposed Typman-KWS (TKWS) architecture built upon MobileNet, specifically designed for efficient KWS on microcontroller unit…

Controllable Singing Voice Synthesis using Phoneme-Level Energy Sequence
Yerin Ryu, Inseop Shin, Chanwoo Kim
https://arxiv.org/abs/2509.07038 https://arxiv…

Controllable Singing Voice Synthesis using Phoneme-Level Energy Sequence
Controllable Singing Voice Synthesis (SVS) aims to generate expressive singing voices reflecting user intent. While recent SVS systems achieve high audio quality, most rely on probabilistic modeling, limiting precise control over attributes such as dynamics. We address this by focusing on dynamic control--temporal loudness variation essential for musical expressiveness--and explicitly condition the SVS model on energy sequences extracted from ground-truth spectrograms, reducing annotation costs…

Prototype: A Keyword Spotting-Based Intelligent Audio SoC for IoT
Huihong Liang, Dongxuan Jia, Youquan Wang, Longtao Huang, Shida Zhong, Luping Xiang, Lei Huang, Tao Yuan
https://arxiv.org/abs/2509.06964

Prototype: A Keyword Spotting-Based Intelligent Audio SoC for IoT
In this demo, we present a compact intelligent audio system-on-chip (SoC) integrated with a keyword spotting accelerator, enabling ultra-low latency, low-power, and low-cost voice interaction in Internet of Things (IoT) devices. Through algorithm-hardware co-design, the system's energy efficiency is maximized. We demonstrate the system's capabilities through a live FPGA-based prototype, showcasing stable performance and real-time voice interaction for edge intelligence applications.

[2025-09-10 Wed (UTC), 11 new articles found for cs.SD Sound]
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Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- Synthetic data enables context-aware bioacoustic sound event detection
Benjamin Hoffman, et al.

Crosslisted article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- On the Contribution of Lexical Features to Speech Emotion Recognition
David Combei

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets
Pedro Ramoneda, Pablo Alonso-Jimenez, Sergio Oramas, Xavier Serra, Dmitry Bogdanov
https://arxiv.org/abs/2509.06936

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets
Music autotagging aims to automatically assign descriptive tags, such as genre, mood, or instrumentation, to audio recordings. Due to its challenges, diversity of semantic descriptions, and practical value in various applications, it has become a common downstream task for evaluating the performance of general-purpose music representations learned from audio data. We introduce a new benchmarking dataset based on the recently published MGPHot dataset, which includes expert musicological annotati…

Continuous Audio Language Models
Rouard Simon, Orsini Manu, Roebel Axel, Zeghidour Neil, D\'efossez Alexandre
https://arxiv.org/abs/2509.06926 https://…

Continuous Audio Language Models
Audio Language Models (ALM) have emerged as the dominant paradigm for speech and music generation by representing audio as sequences of discrete tokens. Yet, unlike text tokens, which are invertible, audio tokens are extracted from lossy codecs with a limited bitrate. As a consequence, increasing audio quality requires generating more tokens, which imposes a trade-off between fidelity and computational cost. We address this issue by studying Continuous Audio Language Models (CALM). These models…

AnalysisGNN: Unified Music Analysis with Graph Neural Networks
Emmanouil Karystinaios, Johannes Hentschel, Markus Neuwirth, Gerhard Widmer
https://arxiv.org/abs/2509.06654 https…

AnalysisGNN: Unified Music Analysis with Graph Neural Networks
Recent years have seen a boom in computational approaches to music analysis, yet each one is typically tailored to a specific analytical domain. In this work, we introduce AnalysisGNN, a novel graph neural network framework that leverages a data-shuffling strategy with a custom weighted multi-task loss and logit fusion between task-specific classifiers to integrate heterogeneously annotated symbolic datasets for comprehensive score analysis. We further integrate a Non-Chord-Tone prediction modu…

The First Voice Timbre Attribute Detection Challenge
Liping Chen, Jinghao He, Zhengyan Sheng, Kong Aik Lee, Zhen-Hua Ling
https://arxiv.org/abs/2509.06635 https://

The First Voice Timbre Attribute Detection Challenge
The first voice timbre attribute detection challenge is featured in a special session at NCMMSC 2025. It focuses on the explainability of voice timbre and compares the intensity of two speech utterances in a specified timbre descriptor dimension. The evaluation was conducted on the VCTK-RVA dataset. Participants developed their systems and submitted their outputs to the organizer, who evaluated the performance and sent feedback to them. Six teams submitted their outputs, with five providing des…

FireRedChat: A Pluggable, Full-Duplex Voice Interaction System with Cascaded and Semi-Cascaded Implementations
Junjie Chen, Yao Hu, Junjie Li, Kangyue Li, Kun Liu, Wenpeng Li, Xu Li, Ziyuan Li, Feiyu Shen, Xu Tang, Manzhen Wei, Yichen Wu, Fenglong Xie, Kaituo Xu, Kun Xie
https://arxiv.org/abs/2509.06502

FireRedChat: A Pluggable, Full-Duplex Voice Interaction System with Cascaded and Semi-Cascaded Implementations
Full-duplex voice interaction allows users and agents to speak simultaneously with controllable barge-in, enabling lifelike assistants and customer service. Existing solutions are either end-to-end, difficult to design and hard to control, or modular pipelines governed by turn-taking controllers that ease upgrades and per-module optimization; however, prior modular frameworks depend on non-open components and external providers, limiting holistic optimization. In this work, we present a complet…

MeanFlow-Accelerated Multimodal Video-to-Audio Synthesis via One-Step Generation
Xiaoran Yang, Jianxuan Yang, Xinyue Guo, Haoyu Wang, Ningning Pan, Gongping Huang
https://arxiv.org/abs/2509.06389

MeanFlow-Accelerated Multimodal Video-to-Audio Synthesis via One-Step Generation
A key challenge in synthesizing audios from silent videos is the inherent trade-off between synthesis quality and inference efficiency in existing methods. For instance, flow matching based models rely on modeling instantaneous velocity, inherently require an iterative sampling process, leading to slow inference speeds. To address this efficiency bottleneck, we introduce a MeanFlow-accelerated model that characterizes flow fields using average velocity, enabling one-step generation and thereby …

DreamAudio: Customized Text-to-Audio Generation with Diffusion Models
Yi Yuan, Xubo Liu, Haohe Liu, Xiyuan Kang, Zhuo Chen, Yuxuan Wang, Mark D. Plumbley, Wenwu Wang
https://arxiv.org/abs/2509.06027

DreamAudio: Customized Text-to-Audio Generation with Diffusion Models
With the development of large-scale diffusion-based and language-modeling-based generative models, impressive progress has been achieved in text-to-audio generation. Despite producing high-quality outputs, existing text-to-audio models mainly aim to generate semantically aligned sound and fall short on precisely controlling fine-grained acoustic characteristics of specific sounds. As a result, users that need specific sound content may find it challenging to generate the desired audio clips. In…

Xi : Uncertainty Supervision for Robust Speaker Embedding
Junjie Li, Kong Aik Lee, Duc-Tuan Truong, Tianchi Liu, Man-Wai Mak
https://arxiv.org/abs/2509.05993 https://

Xi+: Uncertainty Supervision for Robust Speaker Embedding
There are various factors that can influence the performance of speaker recognition systems, such as emotion, language and other speaker-related or context-related variations. Since individual speech frames do not contribute equally to the utterance-level representation, it is essential to estimate the importance or reliability of each frame. The xi-vector model addresses this by assigning different weights to frames based on uncertainty estimation. However, its uncertainty estimation model is …

TSPC: A Two-Stage Phoneme-Centric Architecture for code-switching Vietnamese-English Speech Recognition
Minh N. H. Nguyen, Anh Nguyen Tran, Dung Truong Dinh, Nam Van Vo
https://arxiv.org/abs/2509.05983

TSPC: A Two-Stage Phoneme-Centric Architecture for code-switching Vietnamese-English Speech Recognition
Code-switching (CS) presents a significant challenge for general Auto-Speech Recognition (ASR) systems. Existing methods often fail to capture the subtle phonological shifts inherent in CS scenarios. The challenge is particularly difficult for language pairs like Vietnamese and English, where both distinct phonological features and the ambiguity arising from similar sound recognition are present. In this paper, we propose a novel architecture for Vietnamese-English CS ASR, a Two-Stage Phoneme-C…

[2025-09-09 Tue (UTC), 9 new articles found for cs.SD Sound]
toXiv_bot_toot

Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- The Model Hears You: Audio Language Model Deployments Should Consider the Principle of Least Priv...
Luxi He, Xiangyu Qi, Michel Liao, Inyoung Cheong, Prateek Mittal, Danqi Chen, Peter Henderson

Crosslisted article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- Exploring System Adaptations For Minimum Latency Real-Time Piano Transcription
Patricia Hu, Silvan David Peter, Jan Schl\"uter, Gerhard Widmer

Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- A Voice-based Triage for Type 2 Diabetes using a Conversational Virtual Assistant in the Home Env...
Kelvin Summoogum, Debayan Das, Sathish Kumaran

Latent Acoustic Mapping for Direction of Arrival Estimation: A Self-Supervised Approach
Adrian S. Roman, Iran R. Roman, Juan P. Bello
https://arxiv.org/abs/2507.07066

Latent Acoustic Mapping for Direction of Arrival Estimation: A Self-Supervised Approach
Acoustic mapping techniques have long been used in spatial audio processing for direction of arrival estimation (DoAE). Traditional beamforming methods for acoustic mapping, while interpretable, often rely on iterative solvers that can be computationally intensive and sensitive to acoustic variability. On the other hand, recent supervised deep learning approaches offer feedforward speed and robustness but require large labeled datasets and lack interpretability. Despite their strengths, both me…

Comparative Analysis of CNN and Transformer Architectures with Heart Cycle Normalization for Automated Phonocardiogram Classification
Martin Sondermann, Pinar Bisgin, Niklas Tschorn, Anja Burmann, Christoph M. Friedrich
https://arxiv.org/abs/2507.07058

Comparative Analysis of CNN and Transformer Architectures with Heart Cycle Normalization for Automated Phonocardiogram Classification
The automated classification of phonocardiogram (PCG) recordings represents a substantial advancement in cardiovascular diagnostics. This paper presents a systematic comparison of four distinct models for heart murmur detection: two specialized convolutional neural networks (CNNs) and two zero-shot universal audio transformers (BEATs), evaluated using fixed-length and heart cycle normalization approaches. Utilizing the PhysioNet2022 dataset, a custom heart cycle normalization method tailored to…

A Novel Hybrid Deep Learning Technique for Speech Emotion Detection using Feature Engineering
Shahana Yasmin Chowdhury, Bithi Banik, Md Tamjidul Hoque, Shreya Banerjee
https://arxiv.org/abs/2507.07046

A Novel Hybrid Deep Learning Technique for Speech Emotion Detection using Feature Engineering
Nowadays, speech emotion recognition (SER) plays a vital role in the field of human-computer interaction (HCI) and the evolution of artificial intelligence (AI). Our proposed DCRF-BiLSTM model is used to recognize seven emotions: neutral, happy, sad, angry, fear, disgust, and surprise, which are trained on five datasets: RAVDESS (R), TESS (T), SAVEE (S), EmoDB (E), and Crema-D (C). The model achieves high accuracy on individual datasets, including 97.83% on RAVDESS, 97.02% on SAVEE, 95.10% for …

Advances in Intelligent Hearing Aids: Deep Learning Approaches to Selective Noise Cancellation
Haris Khan, Shumaila Asif, Hassan Nasir
https://arxiv.org/abs/2507.07043

Advances in Intelligent Hearing Aids: Deep Learning Approaches to Selective Noise Cancellation
The integration of artificial intelligence into hearing assistance marks a paradigm shift from traditional amplification-based systems to intelligent, context-aware audio processing. This systematic literature review evaluates advances in AI-driven selective noise cancellation (SNC) for hearing aids, highlighting technological evolution, implementation challenges, and future research directions. We synthesize findings across deep learning architectures, hardware deployment strategies, clinical …

Physics-Informed Direction-Aware Neural Acoustic Fields
Yoshiki Masuyama, Fran\c{c}ois G. Germain, Gordon Wichern, Christopher Ick, Jonathan Le Roux
https://arxiv.org/abs/2507.06826

Physics-Informed Direction-Aware Neural Acoustic Fields
This paper presents a physics-informed neural network (PINN) for modeling first-order Ambisonic (FOA) room impulse responses (RIRs). PINNs have demonstrated promising performance in sound field interpolation by combining the powerful modeling capability of neural networks and the physical principles of sound propagation. In room acoustics, PINNs have typically been trained to represent the sound pressure measured by omnidirectional microphones where the wave equation or its frequency-domain cou…

Data-Balanced Curriculum Learning for Audio Question Answering
Gijs Wijngaard, Elia Formisano, Michele Esposito, Michel Dumontier
https://arxiv.org/abs/2507.06815

Data-Balanced Curriculum Learning for Audio Question Answering
Audio question answering (AQA) requires models to understand acoustic content and perform complex reasoning. Current models struggle with dataset imbalances and unstable training dynamics. This work combines curriculum learning with statistical data balancing to address these challenges. The method labels question difficulty using language models, then trains progressively from easy to hard examples. Statistical filtering removes overrepresented audio categories, and guided decoding constrains …

Revealing the Hidden Temporal Structure of HubertSoft Embeddings based on the Russian Phonetic Corpus
Anastasia Ananeva, Anton Tomilov, Marina Volkova
https://arxiv.org/abs/2507.06794

Revealing the Hidden Temporal Structure of HubertSoft Embeddings based on the Russian Phonetic Corpus
Self-supervised learning (SSL) models such as Wav2Vec 2.0 and HuBERT have shown remarkable success in extracting phonetic information from raw audio without labelled data. While prior work has demonstrated that SSL embeddings encode phonetic features at the frame level, it remains unclear whether these models preserve temporal structure, specifically, whether embeddings at phoneme boundaries reflect the identity and order of adjacent phonemes. This study investigates the extent to which boundar…

Constraint Optimized Multichannel Mixer-limiter Design
Yuancheng Luo, Dmitriy Yamkovoy, Guillermo Garcia
https://arxiv.org/abs/2507.06769 https://

Constraint Optimized Multichannel Mixer-limiter Design
Multichannel audio mixer and limiter designs are conventionally decoupled for content reproduction over loudspeaker arrays due to high computational complexity and run-time costs. We propose a coupled mixer-limiter-envelope design formulated as an efficient linear-constrained quadratic program that minimizes a distortion objective over multichannel gain variables subject to sample mixture constraints. Novel methods for asymmetric constant overlap-add window optimization, objective function appr…

Exploring State-Space-Model based Language Model in Music Generation
Wei-Jaw Lee, Fang-Chih Hsieh, Xuanjun Chen, Fang-Duo Tsai, Yi-Hsuan Yang
https://arxiv.org/abs/2507.06674

Exploring State-Space-Model based Language Model in Music Generation
The recent surge in State Space Models (SSMs), particularly the emergence of Mamba, has established them as strong alternatives or complementary modules to Transformers across diverse domains. In this work, we aim to explore the potential of Mamba-based architectures for text-to-music generation. We adopt discrete tokens of Residual Vector Quantization (RVQ) as the modeling representation and empirically find that a single-layer codebook can capture semantic information in music. Motivated by t…

STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation
Wenxiang Guo, Yu Zhang, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, Zhetao Chen, Wenhao Xu, Fei Wu, Zhou Zhao
https://arxiv.org/abs/2507.06670

STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation
Recent breakthroughs in singing voice synthesis (SVS) have heightened the demand for high-quality annotated datasets, yet manual annotation remains prohibitively labor-intensive and resource-intensive. Existing automatic singing annotation (ASA) methods, however, primarily tackle isolated aspects of the annotation pipeline. To address this fundamental challenge, we present STARS, which is, to our knowledge, the first unified framework that simultaneously addresses singing transcription, alignme…

IMPACT: Industrial Machine Perception via Acoustic Cognitive Transformer
Changheon Han, Yuseop Sim, Hoin Jung, Jiho Lee, Hojun Lee, Yun Seok Kang, Sucheol Woo, Garam Kim, Hyung Wook Park, Martin Byung-Guk Jun
https://arxiv.org/abs/2507.06481

IMPACT: Industrial Machine Perception via Acoustic Cognitive Transformer
Acoustic signals from industrial machines offer valuable insights for anomaly detection, predictive maintenance, and operational efficiency enhancement. However, existing task-specific, supervised learning methods often scale poorly and fail to generalize across diverse industrial scenarios, whose acoustic characteristics are distinct from general audio. Furthermore, the scarcity of accessible, large-scale datasets and pretrained models tailored for industrial audio impedes community-driven res…

MixAssist: An Audio-Language Dataset for Co-Creative AI Assistance in Music Mixing
Michael Clemens, Ana Marasovi\'c
https://arxiv.org/abs/2507.06329 ht…

MixAssist: An Audio-Language Dataset for Co-Creative AI Assistance in Music Mixing
While AI presents significant potential for enhancing music mixing and mastering workflows, current research predominantly emphasizes end-to-end automation or generation, often overlooking the collaborative and instructional dimensions vital for co-creative processes. This gap leaves artists, particularly amateurs seeking to develop expertise, underserved. To bridge this, we introduce MixAssist, a novel audio-language dataset capturing the situated, multi-turn dialogue between expert and amateu…

[2025-07-10 Thu (UTC), 12 new articles found for cs.SD Sound]
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