| MaxBenChrist/awesome_time_series_in_python |
1,811 |
|
0 |
0 |
over 3 years ago |
0 |
|
4 |
|
|
| This curated list contains python packages for time series analysis |
| fraunhoferportugal/tsfel |
758 |
|
0 |
0 |
over 2 years ago |
9 |
August 22, 2023 |
3 |
bsd-3-clause |
Python |
| An intuitive library to extract features from time series. |
| gionanide/Speech_Signal_Processing_and_Classification |
203 |
|
0 |
0 |
over 3 years ago |
0 |
|
3 |
mit |
Python |
| 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]. |
| jsingh811/pyAudioProcessing |
175 |
|
0 |
0 |
over 3 years ago |
12 |
May 20, 2022 |
9 |
gpl-3.0 |
Python |
| Audio feature extraction and classification |
| Project-10/DGM |
167 |
|
0 |
0 |
over 4 years ago |
0 |
|
4 |
other |
C++ |
| Direct Graphical Models (DGM) C++ library, a cross-platform Conditional Random Fields library, which is optimized for parallel computing and includes modules for feature extraction, classification and visualization. |
| ahmetozlu/color_recognition |
148 |
|
0 |
0 |
over 5 years ago |
0 |
|
0 |
mit |
Python |
| Color Recognition on a Webcam Stream / on Video / on a Single Image using K-Nearest Neighbors (KNN) is Trained with Color Histogram Features. |
| holyhao/Baidu-Dogs |
99 |
|
0 |
0 |
almost 9 years ago |
0 |
|
1 |
|
Python |
| Baidu competition for classifying dogs. More information is provided at http://js.baidu.com |
| chsasank/image_features |
60 |
|
0 |
0 |
almost 7 years ago |
0 |
|
0 |
|
Python |
| Extract deep learning features from images using simple python interface |
| zygmuntz/time-series-classification |
59 |
|
0 |
0 |
over 7 years ago |
0 |
|
0 |
apache-2.0 |
Python |
| Classifying time series using feature extraction |
| davpinto/fastknn |
55 |
|
0 |
0 |
over 8 years ago |
0 |
|
1 |
|
R |
| Fast k-Nearest Neighbors Classifier for Large Datasets |
