Audio analysis is useful in many application scenarios. The state-of-the-art
audio analysis approaches assume that the data distribution at training and
deployment time will be the same. However, due to various real-life
environmental factors, the data may encounter drift in its distribution or can
encounter new classes in the late future. Thus, a one-time trained model might
not perform adequately. In this paper, we characterize continual learning (CL)
approaches in audio analysis. In this paper, we characterize continual learning
(CL) approaches, intended to tackle catastrophic forgetting arising due to
drifts. As there is no CL dataset for audio analysis, we use DCASE 2020 to 2023
datasets to create various CL scenarios for audio-based monitoring tasks. We
have investigated the following CL and non-CL approaches: EWC, LwF, SI, GEM,
A-GEM, GDumb, Replay, Naive, cumulative, and joint training. The study is very
beneficial for researchers and practitioners working in the area of audio
analysis for developing adaptive models. We observed that Replay achieved
better results than other methods in the DCASE challenge data. It achieved an
accuracy of 70.12% for the domain incremental scenario and an accuracy of
96.98% for the class incremental scenario.

音频分析中的连续学习方法用于解决因分布漂移导致的灾难性遗忘问题，通过使用 DCASE 数据集探索了多个连续学习方法，在领域逐步增加和类别逐步增加的场景下，Replay 方法取得了最佳结果，分别达到了 70.12% 和 96.98% 的准确率。

关于音频分析的持续学习情境和策略的特征化

Characterizing Continual Learning Scenarios and Strategies for Audio  Analysis

In the U.S., approximately 15-17% of children 2-8 years of age are estimated
to have at least one diagnosed mental, behavioral or developmental disorder.
However, such disorders often go undiagnosed, and the ability to evaluate and
treat disorders in the first years of life is limited. To analyze infant
developmental changes, previous studies have shown advanced ML models excel at
classifying infant and/or parent vocalizations collected using cell phone,
video, or audio-only recording device like LENA. In this study, we pilot test
the audio component of a new infant wearable multi-modal device that we have
developed called LittleBeats (LB). LB audio pipeline is advanced in that it
provides reliable labels for both speaker diarization and vocalization
classification tasks, compared with other platforms that only record audio
and/or provide speaker diarization labels. We leverage wav2vec 2.0 to obtain
superior and more nuanced results with the LB family audio stream. We use a
bag-of-audio-words method with wav2vec 2.0 features to create high-level
visualizations to understand family-infant vocalization interactions. We
demonstrate that our high-quality visualizations capture major types of family
vocalization interactions, in categories indicative of mental, behavioral, and
developmental health, for both labeled and unlabeled LB audio.

研究使用基于 ML 的音频分析方法在新型多模式可穿戴设备 LittleBeats 中进行婴儿语言交互的监测，为早期识别和干预身心健康方面的发展障碍提供了可靠的工具和分析结果。

基于 Wav2vec 2.0 特征的 Bag-of-Audio-Words 方法可视化复杂的家庭婴儿语音序列

Visualizations of Complex Sequences of Family-Infant Vocalizations Using Bag-of-Audio-Words Approach Based on Wav2vec 2.0 Features

This paper introduces a convolutional recurrent network with attention for
speech command recognition. Attention models are powerful tools to improve
performance on natural language, image captioning and speech tasks. The
proposed model establishes a new state-of-the-art accuracy of 94.1% on Google
Speech Commands dataset V1 and 94.5% on V2 (for the 20-commands recognition
task), while still keeping a small footprint of only 202K trainable parameters.
Results are compared with previous convolutional implementations on 5 different
tasks (20 commands recognition (V1 and V2), 12 commands recognition (V1), 35
word recognition (V1) and left-right (V1)). We show detailed performance
results and demonstrate that the proposed attention mechanism not only improves
performance but also allows inspecting what regions of the audio were taken
into consideration by the network when outputting a given category.

本文介绍了一个使用卷积循环神经网络与注意力机制的语音指令识别模型，其创造了 94.1%（V1）和 94.5%（V2）的分类准确率，是同类模型中的新记录，并且该模型仅有 202K 个可以训练的参数，同时其提出的注意力机制不仅提高了性能，还允许可视化网络处理输入音频时所参考的区域。