paper_authors: Vrindha M. K., Geethu V., Anurenjan P. R., Deepak S., Sreeni K. G.
for: 本文旨在检查使用语音分析来诊断阿尔茨heimer病。
methods: 本文使用了不同的算法来分类阿尔茨heimer病，包括语音特征工程和自然语言处理。
results: 根据本文的结论，可以通过考虑语音和语言特征来建立更准确的阿尔茨heimer病分类模型。此外，语音信号可能是诊断 деменция的有用工具，并可能成为诊断阿尔茨heimer病的可靠生物标志。Abstract
In the past decade, there has been a surge in research examining the use of voice and speech analysis as a means of detecting neurodegenerative diseases such as Alzheimer's. Many studies have shown that certain acoustic features can be used to differentiate between normal aging and Alzheimer's disease, and speech analysis has been found to be a cost-effective method of detecting Alzheimer's dementia. The aim of this review is to analyze the various algorithms used in speech-based detection and classification of Alzheimer's disease. A literature survey was conducted using databases such as Web of Science, Google Scholar, and Science Direct, and articles published from January 2020 to the present were included based on keywords such as ``Alzheimer's detection'', "speech," and "natural language processing." The ADReSS, Pitt corpus, and CCC datasets are commonly used for the analysis of dementia from speech, and this review focuses on the various acoustic and linguistic feature engineering-based classification models drawn from 15 studies. Based on the findings of this study, it appears that a more accurate model for classifying Alzheimer's disease can be developed by considering both linguistic and acoustic data. The review suggests that speech signals can be a useful tool for detecting dementia and may serve as a reliable biomarker for efficiently identifying Alzheimer's disease.
摘要
过去一代，有很多研究探讨使用声音和语音分析来诊断神经退化疾病如阿尔茨海默病。许多研究表明，certain acoustic features可以用于 отличать正常老化和阿尔茨海默病，并且语音分析被认为是一种cost-effective的诊断阿尔茨海默病方法。本文的目的是对speech-based detection和分类阿尔茨海默病Algorithms进行分析。通过Web of Science、Google Scholar和Science Direct等数据库，对于2020年1月至当前期间发表的文章进行了文献综述，根据键入关键字“阿尔茨海默病检测”、“语音”和“自然语言处理”进行过滤。ADReSS、Pitt corpus和CCC数据集是用于分析诊断 деменции的常用数据集，本文将Focus on various acoustic and linguistic feature engineering-based classification models from 15 studies。根据本研究的结果，可以通过考虑语音和语言数据来建立更准确的阿尔茨海默病分类模型。该综述表明，speech signals可以用于检测诊断 деменcia，并可能成为阿尔茨海默病的可靠生物标志。
DisCover: Disentangled Music Representation Learning for Cover Song Identification
results: 对比best-performing方法，该研究的 DisCover 框架在 CSI 任务中表现出优于其他方法，并且进行了深入分析，证明了不可分化的重要性。Abstract
In the field of music information retrieval (MIR), cover song identification (CSI) is a challenging task that aims to identify cover versions of a query song from a massive collection. Existing works still suffer from high intra-song variances and inter-song correlations, due to the entangled nature of version-specific and version-invariant factors in their modeling. In this work, we set the goal of disentangling version-specific and version-invariant factors, which could make it easier for the model to learn invariant music representations for unseen query songs. We analyze the CSI task in a disentanglement view with the causal graph technique, and identify the intra-version and inter-version effects biasing the invariant learning. To block these effects, we propose the disentangled music representation learning framework (DisCover) for CSI. DisCover consists of two critical components: (1) Knowledge-guided Disentanglement Module (KDM) and (2) Gradient-based Adversarial Disentanglement Module (GADM), which block intra-version and inter-version biased effects, respectively. KDM minimizes the mutual information between the learned representations and version-variant factors that are identified with prior domain knowledge. GADM identifies version-variant factors by simulating the representation transitions between intra-song versions, and exploits adversarial distillation for effect blocking. Extensive comparisons with best-performing methods and in-depth analysis demonstrate the effectiveness of DisCover and the and necessity of disentanglement for CSI.
摘要
在音乐信息检索（MIR）领域，绘制歌曲标识（CSI）是一项具有挑战性的任务，旨在从庞大的歌曲库中标识查询歌曲的重新录制版本。现有的方法仍然受到高度的内版本变化和 между版本相关性的影响，这是因为模型的版本特定和版本不具有效的因素相互纠缠不清。在这种情况下，我们设定了分离版本特定和版本不具有效的因素的目标，这可以使模型更容易学习查询歌曲未知版本的不变音乐表示。我们使用 causal graph 技术进行分析 CSI 任务，并确定了内版本和 между版本的影响，以阻塞这些影响。为此，我们提出了一个名为 DisCover 的杜陵音乐表示学习框架，它包括两个关键组件：（1）帮助 guid 分离模块（KDM）和（2）整形强化对抗分离模块（GADM）。KDM 尝试将学习的表示与版本特定因素进行分离，而 GADM 则通过模拟版本之间的表示转移，并通过对抗填充来阻塞版本相关的影响。我们对最佳实现和深入分析进行了广泛的比较，并证明了 DisCover 的有效性和分离的必要性。
Improving Domain Generalization for Sound Classification with Sparse Frequency-Regularized Transformer
paper_authors: Honglin Mu, Wentian Xia, Wanxiang Che
for: 提高Transformer模型对不同数据的泛化能力
methods: 限制每个语言序列位置的自注意力响应范围在频率维度上
results: 实现Transformer模型在TAU 2020和Nsynth数据集上的SOTA泛化性能，并且降低了20%的执行时间Abstract
Sound classification models' performance suffers from generalizing on out-of-distribution (OOD) data. Numerous methods have been proposed to help the model generalize. However, most either introduce inference overheads or focus on long-lasting CNN-variants, while Transformers has been proven to outperform CNNs on numerous natural language processing and computer vision tasks. We propose FRITO, an effective regularization technique on Transformer's self-attention, to improve the model's generalization ability by limiting each sequence position's attention receptive field along the frequency dimension on the spectrogram. Experiments show that our method helps Transformer models achieve SOTA generalization performance on TAU 2020 and Nsynth datasets while saving 20% inference time.
摘要
音频分类模型的表现受到外部数据（OOD）的影响，许多方法已经被提议来帮助模型泛化。然而，大多数方法都会增加推理负担或者专注于长期存在的CNN变体，而 transformer 则被证明可以在自然语言处理和计算机视觉任务上超越 CNN。我们提出了 FRITO，一种有效的 regularization 技术，用于限制 transformer 自注意力的每个序列位置的频率维度在 spectrogram 上。实验显示，我们的方法可以帮助 transformer 模型在 TAU 2020 和 Nsynth 数据集上实现 SOTA 的泛化性能，同时节省 20% 的推理时间。
SLMGAN: Exploiting Speech Language Model Representations for Unsupervised Zero-Shot Voice Conversion in GANs
results: 对比其他当前最佳无监督语音变换模型，SLMGAN在自然性和相似性两个指标上表现出色，表明SLM基于的抽象语音模型可以为相关应用提供潜在的优势。Abstract
In recent years, large-scale pre-trained speech language models (SLMs) have demonstrated remarkable advancements in various generative speech modeling applications, such as text-to-speech synthesis, voice conversion, and speech enhancement. These applications typically involve mapping text or speech inputs to pre-trained SLM representations, from which target speech is decoded. This paper introduces a new approach, SLMGAN, to leverage SLM representations for discriminative tasks within the generative adversarial network (GAN) framework, specifically for voice conversion. Building upon StarGANv2-VC, we add our novel SLM-based WavLM discriminators on top of the mel-based discriminators along with our newly designed SLM feature matching loss function, resulting in an unsupervised zero-shot voice conversion system that does not require text labels during training. Subjective evaluation results show that SLMGAN outperforms existing state-of-the-art zero-shot voice conversion models in terms of naturalness and achieves comparable similarity, highlighting the potential of SLM-based discriminators for related applications.
摘要
Translation Notes:* "pre-trained speech language models" (SLMs) ⇒ "预训练的语音语言模型" (SLM)* "text-to-speech synthesis" ⇒ "文本到语音合成"* "voice conversion" ⇒ "语音转换"* "mel-based discriminators" ⇒ "基于mel的探测器"* "SLM feature matching loss function" ⇒ "SLM特征匹配损失函数"* "zero-shot voice conversion" ⇒ "零shot语音转换"* "naturalness" ⇒ "自然性"* "similarity" ⇒ "相似性"