| Renovamen/Speech-Emotion-Recognition |
570 |
|
0 |
0 |
about 3 years ago |
0 |
|
19 |
mit |
Python |
| Speech emotion recognition implemented in Keras (LSTM, CNN, SVM, MLP) | 语音情感识别 |
| anujdutt9/Handwritten-Digit-Recognition-using-Deep-Learning |
158 |
|
0 |
0 |
about 3 years ago |
0 |
|
0 |
mit |
Python |
| Handwritten Digit Recognition using Machine Learning and Deep Learning |
| DataXujing/vehicle-license-plate-recognition |
155 |
|
0 |
0 |
almost 7 years ago |
0 |
|
3 |
mit |
Python |
| :fire: :fire::fire:基于Python的车牌检测和识别系统： |
| HsiehYiChia/Scene-text-recognition |
131 |
|
0 |
0 |
almost 6 years ago |
0 |
|
3 |
mit |
C++ |
| Scene text detection and recognition based on Extremal Region(ER) |
| AlexOuyang/RealTimeFaceRecognition |
45 |
|
0 |
0 |
over 7 years ago |
0 |
|
4 |
|
Python |
| OpenCV Haarcascade and SVM for real time facial tracking and recognition using webcam |
| joyeecheung/digit-detection-recognition |
32 |
|
0 |
0 |
almost 11 years ago |
0 |
|
0 |
|
Python |
| Detects and recognizes digits in the image with AdaBoost and SVM. |
| huajh/action_recognition |
30 |
|
0 |
0 |
over 8 years ago |
0 |
|
0 |
|
Matlab |
| Action Recognition & Categories via Spatial-Temporal Features |
| abhi9716/handwritten-MNIST-digit-recognition |
28 |
|
0 |
0 |
almost 8 years ago |
0 |
|
0 |
mit |
Jupyter Notebook |
| Real time MNIST digit recognition with OpenCV and Support Vector Machine (SVM) algorithm. |
| tzaiyang/SpeechEmoRec |
28 |
|
0 |
0 |
almost 8 years ago |
0 |
|
2 |
|
Python |
| Speech Emotion Recognition Using Deep Convolutional Neural Network and Discriminant Temporal Pyramid Matching |
| praweshd/speech_emotion_recognition |
23 |
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0 |
0 |
over 2 years ago |
0 |
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0 |
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Jupyter Notebook |
| In this project, the performance of speech emotion recognition is compared between two methods (SVM vs Bi-LSTM RNN).Conventional classifiers that uses machine learning algorithms has been used for decades in recognizing emotions from speech. However, in recent years, deep learning methods have taken the center stage and have gained popularity for their ability to perform well without any input hand-crafted features. Speech emotion on sets obtained from RAVDESS corpus is classified using a conventionally used Support Vector Machine (SVM) and its performance is compared to that of a bidirectional long short-term memory (LSTM). |
