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SPEECH EMOTION RECOGNITION- Speech Emotion Recognition With Multiscale Area Attention And Data Augmentation
- Domain: AUDIO CLASSIFICATION
- Category: PYTHON
- Year: 2022
- Project Code: PYAC2101
In Speech Emotion Recognition (SER), Emotional Characteristics Often Appear In Diverse Forms Of Energy Patterns In Spectrograms. Typical Attention Neural Network Classifiers Of SER Are Usually Optimized On A Fixed Attention Granularity. In This Paper, We Apply Multiscale Area Attention In A Deep Convolutional Neural Network To Attend Emotional Characteristics With Varied Granularities And Therefore The Classifier Can Benefit From An Ensemble Of Attentions With Different Scales. To Deal With Data Sparsity, We Conduct Data Augmentation With Vocal Tract Length Perturbation (VTLP) To Improve The Generalization Capability Of The Classifier. Experiments Are Carried Out On The Interactive Emotional Dyadic Motion Capture (IEMOCAP) Dataset. We Achieved 79.34% Weighted Accuracy (WA) And 77.54% Unweighted Accuracy (UA), Which, To The Best Of Our Knowledge, Is The State Of The Art On This Dataset.
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- 2022-03-04
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AUDIO OF SPEAKER RECOGNITION- Automatic Speaker Recognition System Based On Machine Learning Algorithms
- Domain: AUDIO CLASSIFICATION
- Category: PYTHON
- Year: 2022
- Project Code: PYAC2102
Speaker Recognition Is A Technique Used To Automatically Recognize A Speaker From A Recording Of Their Voice Or Speech Utterance. Speaker Recognition Technology Has Improved Over Recent Years And Has Become Inexpensive And And Reliable Method For Person Identification And Verification. Research In The Field Of Speaker Recognition Has Now Spanned Over Five Decades And Has Shown Fruitful Results, However There Is Not Much Work Done With Regards To South African Indigenous Languages. This Paper Presents The Development Of An Automatic Speaker Recognition System That Incorporates Classification And Recognition Of Sepedi Home Language Speakers. Four Classifier Models, Namely, Support Vector Machines, K-Nearest Neighbors, Multilayer Perceptrons (MLP) And Random Forest (RF), Are Trained Using WEKA Data Mining Tool. Auto-WEKA Is Applied To Determine The Best Classifier Model Together With Its Best Hyper-parameters. The Performance Of Each Model Is Evaluated In WEKA Using 10-fold Cross Validation. MLP And RF Yielded Good Accuracy Surpassing The State-of-the-art With An Accuracy Of 97% And 99.9% Respectively, The RF Model Is Then Implemented On A Graphical User Interface For Development Testing.
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- 2022-03-04
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MUSIC GENRE CLASSIFICATION USING DEEP LEARNING- Music Genre Classification Classification Using Deep Learning And Neural Network Algorithms
- Domain: AUDIO CLASSIFICATION
- Category: PYTHON
- Year: 2022
- Project Code: PYAC2103
Categorizing Music Files According To Their Genre Is A Challenging Task In The Area Of Music Information Retrieval (MIR). In This Study, We Compare The Performance Of Two Classes Of Models. The First Is A Deep Learning Approach Wherein A CNN Model Is Trained End-to-end, To Predict The Genre Label Of An Audio Signal, Solely Using Its Spectrogram. The Second Approach Utilizes Hand-crafted Features, Both From The Time Domain And The Frequency Domain. We Train Four Traditional Machine Learning Classifiers With These Features And Compare Their Performance. The Features That Contribute The Most Towards This Multi-class Classification Task Are Identified. The Experiments Are Conducted On The Audio Set Data Set And We Report An AUC Value Of 0.894 For An Ensemble Classifier Which Combines The Two Proposed Approaches.
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- 2022-03-04
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SPEECH TO TEXT CONVERTION USING PYTHON- Design Of Voice To Text Conversion And Management Program Based On Google Cloud Speech API
- Domain: AUDIO CLASSIFICATION
- Category: PYTHON
- Year: 2022
- Project Code: PYAC2104
Sexual Crime, Including Sexual Harassment And Sex Assault, Is Prevalent. In Particular, The Number Of Reported Cases Of Sexual Crimes Occurring In The Workplace Is Steadily Increasing. Victims Of Sexual Crime Are Required To Prove The Fact Of The Damage, But It Is Not Easy To Prove The Evidence, So The Sex Offenders Are Often Not Punished Properly Because Of Insufficient Evidence. In This Paper, We Design A Recording Service Called CCVoice. It Uses Mobile Devices To Record Everyday Life. At The Same Time, It Converts The Recorded File To Text Using Google Cloud Speech API And Save The Text File. Therefore, It Is Possible To Easily Obtain Voice Evidence When A User Is Suddenly Sexually Abused Such As Sexual Harassment Or Sex Assault.
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- 2022-03-04
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