Pike渔市场's repositories
awesome-point-cloud-analysis
A list of papers and datasets about point cloud analysis (processing)
deeplearningbook-chinese
Deep Learning Book Chinese Translation
latent_3d_points
Auto-encoding & Generating 3D Point-Clouds.
lihang-code
《统计学习方法》的代码实现
Point-Cloud-GAN
The code for Point Cloud GAN
python-summary
日常工作中python包使用总结
pytorch-PCN
Pytorch implementation of PCN
RL-Project-2019
Reimplementation of RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion
RosePrisma
使用深度学习算法将玫瑰花图片内容和另一幅图片的风格融合在一起
Speech_Signal_Processing_and_Classification
Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].
SuperDouble.github.io
个人博客网站
tensorflow-tutorial
Example TensorFlow codes and Caicloud TensorFlow as a Service dev environment.