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extraordinaryzk's Projects

aljabr-1

Methods in Algebra (Volume 1): A Chinese textbook on Algebra

d2l-en

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge.

d2l-zh

《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被55个国家的300所大学用于教学。

machine-learning

:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归

美国大学生数学建模竞赛、全国大学生数学建模竞赛、华为杯研究生数学建模、数学建模美赛论文，数学建模国赛论文、LaTeX模板、国赛LaTeX模板、美赛LaTeX模板、mathorcup历年论文、研究生数学建模历年论文、电工杯、华中赛、APMCM亚太地区数学建模、深圳杯、中青杯、华东杯、数维杯、东三省数学建模、认证杯、数学建模书籍、数学建模算法、国赛评阅要点、数学建模word模板、软件模型算法汇总、MATLAB算法、常用Matlab代码、智能算法、现代的算法、图论算法、优化算法

matlab

做语音识别时的matlab工程

mlofandrew-ng

吴恩达机器学习课程的讲义，欢迎大家一起学习

nativejson-benchmark

C/C++ JSON parser/generator benchmark

paper-reading

深度学习经典、新论文逐段精读

qt-creator

A cross-platform Qt IDE

sota-point-cloud

🔥[IEEE TPAMI 2020] Deep Learning for 3D Point Clouds: A Survey

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].

speechsignalprocessingcourse

语音信号处理实验教程（MATLAB源代码）

whut_cumcm20

武汉理工大学2020数学建模国赛国二/美赛国二/数维杯国一/亚太国二/校内集训/优秀论文等资料

extraordinaryzk Goto Github PK

extraordinaryzk's Projects

aljabr-1

cumcmthesis

d2l-en

d2l-zh