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Speech-Simulation-Tools

语音增强领域的相关数据仿真工具和方法汇总--持续更新

1：MASG, https://github.com/vipchengrui/MASG
功能实现：房间声学中的麦克风阵列语音发生器(MASG)，仿真室内环境下的麦克风阵列采集数据

2：gpuRIR, https://github.com/DavidDiazGuerra/gpuRIR
功能实现：gpu加速的RIR仿真

FAST-RIR: Fast neural diffuse room impulse response generator
FAST-RIR：快速神经扩散房间脉冲响应发生器
链接：https://arxiv.org/abs/2110.04057
作者：Anton Ratnarajah,Shi-Xiong Zhang,Meng Yu,Zhenyu Tang,Dinesh Manocha,Dong Yu
机构： University of Maryland, College Park, MD, USA, Tencent AI Lab, Bellevue, WA, USA
备注：https://anton-jeran.github.io/FRIR/ https://github.com/anton-jeran/FAST-RIR

3：StoRIR，https://github.com/SRPOL-AUI/storir
功能实现：一种不同于RIR的随机房间脉冲响应生成方法，用于机器学习应用中的音频数据增强，不需要知道房间尺寸和声源位置

4：RIR，https://github.com/ehabets/RIR-Generator
功能描述：基于matlab版本的RIR生成算法仿真

5: DNS-Challenge,https://github.com/microsoft/DNS-Challenge
功能实现：微软开源的DNS挑战赛的数据和仿真脚本，可以直接进行数据的仿真，包括加混响和噪声

6：ConferenceSpeech2021,https://github.com/ConferencingSpeech/ConferencingSpeech2021/tree/master/eval
功能实现：腾讯组织的麦克风阵列语音增强比赛，其中的eval可以批处理生成pesq,stoi，si-sdr等客观评价指标，并输出文件，非常有用。
注：非常好的多通道仿真数据生成脚本，可以根据干净语音、噪声和冲击响应生成响应的多通道数据，包括混响参考数据和非混响参考数据，以及对应的带噪语音数据。 1：仿真训练所需数据时，给出干净语音，多通道冲击响应和噪声后，即可仿真生成多通道带噪混响语音； 2：训练时，默认为8通道数据，每个通道6s，配置时train.yaml中val_chun和train_chunk均为6 pyrirgen库备用安装工程：https://github.com/YoungJay0612/rir-generator-pyrirgen

7: AECMOS: A speech quality assessment metric for echo impairment
标题：AECMOS：一种回声损伤的语音质量评估指标
链接：https://arxiv.org/abs/2110.03010
作者：Marju Purin,Sten Sootla,Mateja Sponza,Ando Saabas,Ross Cutler
机构：Microsoft Corporation

Speech Enhancement Measurement

link：https://github.com/IMLHF/Speech-Enhancement-Measures
speech enhancement metrics：CSIG, CBAK, CMOS, SSNR, PESQ, STOI, ESTOI, SNR, IS, LLR, WSS

link:https://github.com/kamo-naoyuki/pySIIB
pySIIB: A python implementation of speech intelligibility in bits (SIIB)

Audio Generation

Audio generation using diffusion models, in PyTorch.基于diffusion模型的音频生成
link: https://github.com/archinetai/audio-diffusion-pytorch#experiments

Feature Extraction 特征提取

link: https://github.com/YoungJay0612/padasip
通过Python进行Adaptive Signal Processing

link: https://github.com/bootphon/shennong
Shennong: A Python toolbox for speech features extraction

link：https://github.com/ZhihaoDU/speech_feature_extractor
GFCC等特征提取

Loss Compute

Link: https://github.com/csteinmetz1/auraloss
auraloss: A collection of audio-focused loss functions in PyTorch.

Data Augmentation 数据增强

2021-8-9

paper title: SpecMix : A Mixed Sample Data Augmentation method for Training withTime-Frequency Domain Features
paper title: SpecMix：一种利用时频域特征训练的混合样本数据增强方法
链接：https://arxiv.org/abs/2108.03020
作者：Gwantae Kim,David K. Han,Hanseok Ko
github： https://github.com/GT-KIM/specmix
机构：Korea University, South Korea, Drexel University, USA
备注：Accepted to interspeech 2021

muda: https://github.com/bmcfee/muda https://muda.readthedocs.io/en/latest/
SpecAugment: https://github.com/DemisEom/SpecAugment

Facebook-Augly: https://github.com/facebookresearch/AugLy
Introc: AugLy is a data augmentations library that currently supports four modalities (audio, image, text & video) and over 100 augmentations.

Asteroid-team: https://github.com/asteroid-team/torch-audiomentations
Introc: Audio data augmentation in PyTorch. Inspired by audiomentations.

Audiomentation: https://github.com/iver56/audiomentations
Introc: A Python library for audio data augmentation. Inspired by albumentations（图像数据增强）.