Canonical Correlation Analysis (CCA) has been widely applied to jointly embed
multiple views of data in a maximally correlated latent space. However, the
alignment between various data perspectives, which is required by traditional
approaches, is unclear in many practical cases. In this work we propose a new
framework Aligned Canonical Correlation Analysis (ACCA), to address this
challenge by iteratively solving the alignment and multi-view embedding.

提出了一种新的框架 Aligned Canonical Correlation Analysis (ACCA)，通过迭代地解决对齐和多视角嵌入，解决了多数实际情况中需要的传统方法中不清晰的各种数据视角之间的对齐问题。

朝着对齐的规范相关分析的初步构想和概念验证结果

Towards Aligned Canonical Correlation Analysis: Preliminary Formulation  and Proof-of-Concept Results

Machine hearing of the environmental sound is one of the important issues in
the audio recognition domain. It gives the machine the ability to discriminate
between the different input sounds that guides its decision making. In this
work we exploit the self-supervised contrastive technique and a shallow 1D CNN
to extract the distinctive audio features (audio representations) without using
any explicit annotations.We generate representations of a given audio using
both its raw audio waveform and spectrogram and evaluate if the proposed
learner is agnostic to the type of audio input. We further use canonical
correlation analysis (CCA) to fuse representations from the two types of input
of a given audio and demonstrate that the fused global feature results in
robust representation of the audio signal as compared to the individual
representations. The evaluation of the proposed technique is done on both
ESC-50 and UrbanSound8K. The results show that the proposed technique is able
to extract most features of the environmental audio and gives an improvement of
12.8% and 0.9% on the ESC-50 and UrbanSound8K datasets respectively.

利用自监督对比技术和浅层 1D CNN 提取环境音频的显著特征，进一步使用规范相关分析（CCA）来融合来自给定音频的两种输入类型的表示，并证明融合的全局特征相对于单独的表示具有更强的鲁棒性。在 ESC-50 和 UrbanSound8K 上的评估表明，该技术能够提取环境声音的大部分特征，并分别在 ESC-50 和 UrbanSound8K 数据集上获得了 12.8％和 0.9％的改进。

对比环境声音表示学习

Contrastive Environmental Sound Representation Learning

Distributional semantics offers new ways to study the semantics of
morphology. This study focuses on the semantics of noun singulars and their
plural inflectional variants in English. Our goal is to compare two models for
the conceptualization of plurality. One model (FRACSS) proposes that all
singular-plural pairs should be taken into account when predicting plural
semantics from singular semantics. The other model (CCA) argues that
conceptualization for plurality depends primarily on the semantic class of the
base word. We compare the two models on the basis of how well the speech signal
of plural tokens in a large corpus of spoken American English aligns with the
semantic vectors predicted by the two models. Two measures are employed: the
performance of a form-to-meaning mapping and the correlations between form
distances and meaning distances. Results converge on a superior alignment for
CCA. Our results suggest that usage-based approaches to pluralization in which
a given word's own semantic neighborhood is given priority outperform theories
according to which pluralization is conceptualized as a process building on
high-level abstraction. We see that what has often been conceived of as a
highly abstract concept, [+plural], is better captured via a family of
mid-level partial generalizations.

研究了英语名词复数和单数的语义，比较了基于 FRACSS 和 CCA 这两种概念化复数的模型，并在大语料上验证了语音信息和语意向量的预测关系，结果表明基于语用的语意表示方法的效果更好。