SpecMix : A Mixed Sample Data Augmentation method for Training withTime-Frequency Domain Features

用于虚假语音检测的基于随机 Specmix 的多视角信息融合 Res2Net

Multi-perspective Information Fusion Res2Net with RandomSpecmix for Fake Speech Detection

本文提出结合随机Specmix数据增强与多视角信息融合的MPIF-Res2Net模型，通过增强频域信息定位与减少冗余干扰，有效提升了低质量场景下虚假语音检测的泛化与判别能力。

A study on cross-corpus speech emotion recognition and data augmentation

Models that can handle a wide range of speakers and acoustic conditions are essential in speech emotion recognition (SER). Often, these models tend to show mixed results when presented with speakers or acoustic conditions that were not visible during training. This paper investigates the impact of cross-corpus data complementation and data augmentation on the performance of SER models in matched (test-set from same corpus) and mismatched (test-set from different corpus) conditions. Investigations using six emotional speech corpora that include single and multiple speakers as well as variations…

Sample Mixed-Based Data Augmentation for Domestic Audio Tagging

Audio tagging has attracted increasing attention since last decade and has various potential applications in many fields. The objective of audio tagging is to predict the labels of an audio clip. Recently deep learning methods have been applied to audio tagging and have achieved state-of-the-art performance, which provides a poor generalization ability on new data. However due to the limited size of audio tagging data such as DCASE data, the trained models tend to result in overfitting of the network. Previous data augmentation methods such as pitch shifting, time stretching and adding backgro…

Data Augmentation for Instrument Classification Robust to Audio Effects

Reusing recorded sounds (sampling) is a key component in Electronic Music Production (EMP), which has been present since its early days and is at the core of genres like hip-hop or jungle. Commercial and non-commercial services allow users to obtain collections of sounds (sample packs) to reuse in their compositions. Automatic classification of one-shot instrumental sounds allows automatically categorising the sounds contained in these collections, allowing easier navigation and better characterisation. Automatic instrument classification has mostly targeted the classification of unprocessed i…

医学音频分类中的合成数据增强：初步评估

Synthetic Data Augmentation for Medical Audio Classification: A Preliminary Evaluation

本研究评估了合成数据增强在医学音频分类中的效果，发现仅在模型集合中应用增强时，F1分数有所提升，但标准CNN分类器未见明显性能提升。

从强噪声混合语音中识别关键词

Spot keywords from very noisy and mixed speech

针对强干扰语音及多关键词混合的高难度关键词识别场景，提出Mix Training训练策略，经Google Speech Command数据集验证，该方法效果优于标准数据增强和mixup训练。