cs.SD语音【1】Enhancing DMI Interactions by Integrating Haptic Feedback for Intricate Vibrato Technique标题：通过集成触觉反馈来增强触觉互动复杂颤音技术链接：https://arxiv.org/abs/2405.10502作者：Ziyue Piao,Christian Frisson,Bavo Van Kerrebroeck,Marcelo M. Wanderley摘要：本文研究了力反馈在数字乐器中的集成，特别是使用触觉反馈控制器来评估复杂颤音技术的再现。我们介绍我们的系统颤音调制使用力反馈，由弯曲援助（基于Web的音序器平台，使用预先设计的触觉反馈模型）和TorqueTuner（开源1自由度（DoF）旋转触觉设备，用于生成可编程的触觉效果）。我们设计了一个正式的用户研究，以评估每个触觉模式对用户体验的影响，在颤音模仿任务。20名受过音乐训练的参与者使用四个Likert量表评分对三种触觉模式（平滑，制动和弹簧）的用户体验进行了评分：舒适度，灵活性，易于控制和任务的帮助性。最后，我们请参与者分享他们的想法。我们的研究表明，虽然弹簧模式可以帮助轻颤音，触觉模式的偏好根据音乐训练背景而有所不同。这强调了在可编程设计中需要适应性强的任务界面和灵活的触觉反馈。摘要：This paper investigates the integration of force feedback in Digital Musical Instruments (DMI), specifically evaluating the reproduction of intricate vibrato techniques using haptic feedback controllers. We introduce our system for vibrato modulation using force feedback, composed of Bend-aid (a web-based sequencer platform using pre-designed haptic feedback models) and TorqueTuner (an open-source 1 Degree-of-Freedom (DoF) rotary haptic device for generating programmable haptic effects). We designed a formal user study to assess the impact of each haptic mode on user experience in a vibrato mimicry task. Twenty musically trained participants rated their user experience for the three haptic modes (Smooth, Detent, and Spring) using four Likert-scale scores: comfort, flexibility, ease of control, and helpfulness for the task. Finally, we asked participants to share their reflections. Our research indicates that while the Spring mode can help with light vibrato, preferences for haptic modes vary based on musical training background. This emphasizes the need for adaptable task interfaces and flexible haptic feedback in DMI design. eess.AS音频处理【1】 Distinctive and Natural Speaker Anonymization via Singular Value Transformation-assisted Matrix标题：通过奇异值变换辅助矩阵实现独特且自然的说话者解析链接：https://arxiv.org/abs/2405.10786作者：Jixun Yao,Qing Wang,Pengcheng Guo,Ziqian Ning,Lei Xie备注：Accepted by IEEE/ACM Transactions on Audio, Speech, and Language Processing摘要：None摘要：Speaker anonymization is an effective privacy protection solution that aims to conceal the speaker's identity while preserving the naturalness and distinctiveness of the original speech. Mainstream approaches use an utterance-level vector from a pre-trained automatic speaker verification (ASV) model to represent speaker identity, which is then averaged or modified for anonymization. However, these systems suffer from deterioration in the naturalness of anonymized speech, degradation in speaker distinctiveness, and severe privacy leakage against powerful attackers. To address these issues and especially generate more natural and distinctive anonymized speech, we propose a novel speaker anonymization approach that models a matrix related to speaker identity and transforms it into an anonymized singular value transformation-assisted matrix to conceal the original speaker identity. Our approach extracts frame-level speaker vectors from a pre-trained ASV model and employs an attention mechanism to create a speaker-score matrix and speaker-related tokens. Notably, the speaker-score matrix acts as the weight for the corresponding speaker-related token, representing the speaker's identity. The singular value transformation-assisted matrix is generated by recomposing the decomposed orthonormal eigenvectors matrix and non-linear transformed singular through Singular Value Decomposition (SVD). Experiments on VoicePrivacy Challenge datasets demonstrate the effectiveness of our approach in protecting speaker privacy under all attack scenarios while maintaining speech naturalness and distinctiveness.
【2】 Implementation of the Feedforward Multichannel Virtual Sensing Active Noise Control (MVANC) by Using MATLAB标题：利用VB实现前向多通道虚拟感知主动噪音控制（MVACN）链接：https://arxiv.org/abs/2405.10510作者：Boxiang Wang摘要：多通道虚拟感测有源噪声控制（MVANC）方法是一种先进的方法，其可以在远离物理误差麦克风的特定虚拟位置处提供宽的静默区域。目前，可用于MVANC算法的开源程序很少。这项工作提出了一个MATLAB代码的MVANC方法，利用多通道滤波-x最小均方（MCFxLMS）算法。该代码被设计为适用于具有任何数量的通道的系统。代码可以在GitHub上找到。摘要：The multichannel virtual sensing active noise control (MVANC) methodology is an advanced approach that may provide a wide area of silence at specific virtual positions that are distant from the physical error microphones. Currently, there is a scarcity of open-source programs available for the MVANC algorithm. This work presents a MATLAB code for the MVANC approach, utilizing the multichannel filtered-x least mean square (MCFxLMS) algorithm. The code is designed to be applicable to systems with any number of channels. The code can be found on GitHub.
每日语音速递
