Steven Jack's repositories
Sperker_recognition_629
软件工程作业
zhoupeiyuan_Mechanics_competition
周培源力学竞赛资料分享
ChineseChess-AlphaZero
Implement AlphaZero/AlphaGo Zero methods on Chinese chess.
Database-homework
火星叔叔的欢乐假期
hello-world
Just another repository
pacman_homework
AI_homework
-Tianchi_winter_charging2021
记录自己寒假的天池AI学习
996.ICU
Repo for counting stars and contributing. Press F to pay respect to glorious developers.
caffe
Caffe: a fast open framework for deep learning.
dockerbook-code
The code and configuration examples from The Docker Book (http://www.dockerbook.com)
edX-CS188.1x-Artificial-Intelligence
Projects from the edX (BerkleyX) course: CS188.1x Artificial Intelligence
Huawei-Challenge-Speaker-Identification
Trained speaker embedding deep learning models and evaluation pipelines in pytorch and tesorflow for speaker recognition.
learn-python3
Learn Python 3 Sample Code
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].
Start_Leetcode_caishunzhe
尝试每天写3题，不求多
wechat-public-account-push
微信公众号推送-给女朋友的浪漫