arXiv - CS - Sound最新文献_第5页

DAISY: Data Adaptive Self-Supervised Early Exit for Speech Representation Models DAISY：语音表征模型的数据自适应自监督早期退出

arXiv - CS - Sound Pub Date : 2024-06-08 DOI: arxiv-2406.05464

Tzu-Quan Lin, Hung-yi Lee, Hao Tang

引用次数: 0

Mmm whatcha say? Uncovering distal and proximal context effects in first and second-language word perception using psychophysical reverse correlation 嗯，你说什么？利用心理物理反向相关性揭示第一语言和第二语言词汇感知中的远近语境效应

arXiv - CS - Sound Pub Date : 2024-06-08 DOI: arxiv-2406.05515

Paige Tuttösí, H. Henny Yeung, Yue Wang, Fenqi Wang, Guillaume Denis, Jean-Julien Aucouturier, Angelica Lim

{"title":"Mmm whatcha say? Uncovering distal and proximal context effects in first and second-language word perception using psychophysical reverse correlation","authors":"Paige Tuttösí, H. Henny Yeung, Yue Wang, Fenqi Wang, Guillaume Denis, Jean-Julien Aucouturier, Angelica Lim","doi":"arxiv-2406.05515","DOIUrl":"https://doi.org/arxiv-2406.05515","url":null,"abstract":"Acoustic context effects, where surrounding changes in pitch, rate or timbre\u0000influence the perception of a sound, are well documented in speech perception,\u0000but how they interact with language background remains unclear. Using a\u0000reverse-correlation approach, we systematically varied the pitch and speech\u0000rate in phrases around different pairs of vowels for second language (L2)\u0000speakers of English (/i/-/I/) and French (/u/-/y/), thus reconstructing, in a\u0000data-driven manner, the prosodic profiles that bias their perception. Testing\u0000English and French speakers (n=25), we showed that vowel perception is in fact\u0000influenced by conflicting effects from the surrounding pitch and speech rate: a\u0000congruent proximal effect 0.2s pre-target and a distal contrastive effect up to\u00001s before; and found that L1 and L2 speakers exhibited strikingly similar\u0000prosodic profiles in perception. We provide a novel method to investigate\u0000acoustic context effects across stimuli, timescales, and acoustic domain.","PeriodicalId":501178,"journal":{"name":"arXiv - CS - Sound","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141509127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the Benefits of Tokenization of Discrete Acoustic Units 探索离散声学单元标记化的益处

arXiv - CS - Sound Pub Date : 2024-06-08 DOI: arxiv-2406.05547

Avihu Dekel, Raul Fernandez

引用次数: 0

VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term Modeling VidMuse：具有长期短期建模功能的简单视频音乐生成框架

arXiv - CS - Sound Pub Date : 2024-06-06 DOI: arxiv-2406.04321

Zeyue Tian, Zhaoyang Liu, Ruibin Yuan, Jiahao Pan, Xiaoqiang Huang, Qifeng Liu, Xu Tan, Qifeng Chen, Wei Xue, Yike Guo

引用次数: 0

Operational Latent Spaces 业务潜空间

arXiv - CS - Sound Pub Date : 2024-06-04 DOI: arxiv-2406.02699

Scott H. Hawley, Austin R. Tackett

引用次数: 0

Searching For Music Mixing Graphs: A Pruning Approach 搜索音乐混合图：剪枝法

arXiv - CS - Sound Pub Date : 2024-06-03 DOI: arxiv-2406.01049

Sungho Lee, Marco A. Martínez-Ramírez, Wei-Hsiang Liao, Stefan Uhlich, Giorgio Fabbro, Kyogu Lee, Yuki Mitsufuji

引用次数: 0

DITTO-2: Distilled Diffusion Inference-Time T-Optimization for Music Generation DITTO-2：用于音乐生成的蒸馏扩散推理-时间 T 优化技术

arXiv - CS - Sound Pub Date : 2024-05-30 DOI: arxiv-2405.20289

Zachary Novack, Julian McAuley, Taylor Berg-Kirkpatrick, Nicholas Bryan

{"title":"DITTO-2: Distilled Diffusion Inference-Time T-Optimization for Music Generation","authors":"Zachary Novack, Julian McAuley, Taylor Berg-Kirkpatrick, Nicholas Bryan","doi":"arxiv-2405.20289","DOIUrl":"https://doi.org/arxiv-2405.20289","url":null,"abstract":"Controllable music generation methods are critical for human-centered\u0000AI-based music creation, but are currently limited by speed, quality, and\u0000control design trade-offs. Diffusion Inference-Time T-optimization (DITTO), in\u0000particular, offers state-of-the-art results, but is over 10x slower than\u0000real-time, limiting practical use. We propose Distilled Diffusion\u0000Inference-Time T -Optimization (or DITTO-2), a new method to speed up\u0000inference-time optimization-based control and unlock faster-than-real-time\u0000generation for a wide-variety of applications such as music inpainting,\u0000outpainting, intensity, melody, and musical structure control. Our method works\u0000by (1) distilling a pre-trained diffusion model for fast sampling via an\u0000efficient, modified consistency or consistency trajectory distillation process\u0000(2) performing inference-time optimization using our distilled model with\u0000one-step sampling as an efficient surrogate optimization task and (3) running a\u0000final multi-step sampling generation (decoding) using our estimated noise\u0000latents for best-quality, fast, controllable generation. Through thorough\u0000evaluation, we find our method not only speeds up generation over 10-20x, but\u0000simultaneously improves control adherence and generation quality all at once.\u0000Furthermore, we apply our approach to a new application of maximizing text\u0000adherence (CLAP score) and show we can convert an unconditional diffusion model\u0000without text inputs into a model that yields state-of-the-art text control.\u0000Sound examples can be found at https://ditto-music.github.io/ditto2/.","PeriodicalId":501178,"journal":{"name":"arXiv - CS - Sound","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141187830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LLMs Meet Multimodal Generation and Editing: A Survey LLM 满足多模态生成和编辑：调查

arXiv - CS - Sound Pub Date : 2024-05-29 DOI: arxiv-2405.19334

Yingqing He, Zhaoyang Liu, Jingye Chen, Zeyue Tian, Hongyu Liu, Xiaowei Chi, Runtao Liu, Ruibin Yuan, Yazhou Xing, Wenhai Wang, Jifeng Dai, Yong Zhang, Wei Xue, Qifeng Liu, Yike Guo, Qifeng Chen

{"title":"LLMs Meet Multimodal Generation and Editing: A Survey","authors":"Yingqing He, Zhaoyang Liu, Jingye Chen, Zeyue Tian, Hongyu Liu, Xiaowei Chi, Runtao Liu, Ruibin Yuan, Yazhou Xing, Wenhai Wang, Jifeng Dai, Yong Zhang, Wei Xue, Qifeng Liu, Yike Guo, Qifeng Chen","doi":"arxiv-2405.19334","DOIUrl":"https://doi.org/arxiv-2405.19334","url":null,"abstract":"With the recent advancement in large language models (LLMs), there is a\u0000growing interest in combining LLMs with multimodal learning. Previous surveys\u0000of multimodal large language models (MLLMs) mainly focus on understanding. This\u0000survey elaborates on multimodal generation across different domains, including\u0000image, video, 3D, and audio, where we highlight the notable advancements with\u0000milestone works in these fields. Specifically, we exhaustively investigate the\u0000key technical components behind methods and multimodal datasets utilized in\u0000these studies. Moreover, we dig into tool-augmented multimodal agents that can\u0000use existing generative models for human-computer interaction. Lastly, we also\u0000comprehensively discuss the advancement in AI safety and investigate emerging\u0000applications as well as future prospects. Our work provides a systematic and\u0000insightful overview of multimodal generation, which is expected to advance the\u0000development of Artificial Intelligence for Generative Content (AIGC) and world\u0000models. A curated list of all related papers can be found at\u0000https://github.com/YingqingHe/Awesome-LLMs-meet-Multimodal-Generation","PeriodicalId":501178,"journal":{"name":"arXiv - CS - Sound","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Fusion Architecture for PD Detection Using Semi-Supervised Speech Embeddings 一种利用半监督语音嵌入进行 PD 检测的新型融合架构

arXiv - CS - Sound Pub Date : 2024-05-21 DOI: arxiv-2405.17206

Tariq Adnan, Abdelrahman Abdelkader, Zipei Liu, Ekram Hossain, Sooyong Park, MD Saiful Islam, Ehsan Hoque

{"title":"A Novel Fusion Architecture for PD Detection Using Semi-Supervised Speech Embeddings","authors":"Tariq Adnan, Abdelrahman Abdelkader, Zipei Liu, Ekram Hossain, Sooyong Park, MD Saiful Islam, Ehsan Hoque","doi":"arxiv-2405.17206","DOIUrl":"https://doi.org/arxiv-2405.17206","url":null,"abstract":"We present a framework to recognize Parkinson's disease (PD) through an\u0000English pangram utterance speech collected using a web application from diverse\u0000recording settings and environments, including participants' homes. Our dataset\u0000includes a global cohort of 1306 participants, including 392 diagnosed with PD.\u0000Leveraging the diversity of the dataset, spanning various demographic\u0000properties (such as age, sex, and ethnicity), we used deep learning embeddings\u0000derived from semi-supervised models such as Wav2Vec 2.0, WavLM, and ImageBind\u0000representing the speech dynamics associated with PD. Our novel fusion model for\u0000PD classification, which aligns different speech embeddings into a cohesive\u0000feature space, demonstrated superior performance over standard\u0000concatenation-based fusion models and other baselines (including models built\u0000on traditional acoustic features). In a randomized data split configuration,\u0000the model achieved an Area Under the Receiver Operating Characteristic Curve\u0000(AUROC) of 88.94% and an accuracy of 85.65%. Rigorous statistical analysis\u0000confirmed that our model performs equitably across various demographic\u0000subgroups in terms of sex, ethnicity, and age, and remains robust regardless of\u0000disease duration. Furthermore, our model, when tested on two entirely unseen\u0000test datasets collected from clinical settings and from a PD care center,\u0000maintained AUROC scores of 82.12% and 78.44%, respectively. This affirms the\u0000model's robustness and it's potential to enhance accessibility and health\u0000equity in real-world applications.","PeriodicalId":501178,"journal":{"name":"arXiv - CS - Sound","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141166241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing DMI Interactions by Integrating Haptic Feedback for Intricate Vibrato Technique 通过整合触觉反馈增强复杂颤音技术的 DMI 互动

arXiv - CS - Sound Pub Date : 2024-05-17 DOI: arxiv-2405.10502

Ziyue Piao, Christian Frisson, Bavo Van Kerrebroeck, Marcelo M. Wanderley

{"title":"Enhancing DMI Interactions by Integrating Haptic Feedback for Intricate Vibrato Technique","authors":"Ziyue Piao, Christian Frisson, Bavo Van Kerrebroeck, Marcelo M. Wanderley","doi":"arxiv-2405.10502","DOIUrl":"https://doi.org/arxiv-2405.10502","url":null,"abstract":"This paper investigates the integration of force feedback in Digital Musical\u0000Instruments (DMI), specifically evaluating the reproduction of intricate\u0000vibrato techniques using haptic feedback controllers. We introduce our system\u0000for vibrato modulation using force feedback, composed of Bend-aid (a web-based\u0000sequencer platform using pre-designed haptic feedback models) and TorqueTuner\u0000(an open-source 1 Degree-of-Freedom (DoF) rotary haptic device for generating\u0000programmable haptic effects). We designed a formal user study to assess the\u0000impact of each haptic mode on user experience in a vibrato mimicry task. Twenty\u0000musically trained participants rated their user experience for the three haptic\u0000modes (Smooth, Detent, and Spring) using four Likert-scale scores: comfort,\u0000flexibility, ease of control, and helpfulness for the task. Finally, we asked\u0000participants to share their reflections. Our research indicates that while the\u0000Spring mode can help with light vibrato, preferences for haptic modes vary\u0000based on musical training background. This emphasizes the need for adaptable\u0000task interfaces and flexible haptic feedback in DMI design.","PeriodicalId":501178,"journal":{"name":"arXiv - CS - Sound","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141149138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0