Audio Fraud Detection Model -- 2

@nrajuu07

I'm an audio ML specialist experienced in speech classification and fraud detection systems. I'll build an end-to-end pipeline for your MP3 call corpus — preprocessing audio, extracting meaningful acoustic features (MFCCs, spectrograms, prosodic elements), and training a robust multi-class classifier (CNN, RNN, Transformer, or hybrid architecture) to detect Phishing, Robocalls, and Telemarketing scams with high precision and recall. Deliverables include a fully evaluated model with precision, recall, F1 scores, and confusion matrices per fraud type, an inference script or lightweight REST service accepting MP3 files and returning predicted class with confidence scores, saved model weights, and clean, reproducible Python code using TensorFlow or PyTorch. I have relevant experience in speech analytics and fraud detection projects; my preferred toolkit is PyTorch with librosa for audio preprocessing and huggingface transformers for advanced architectures. Ready to discuss specific architecture choices and milestones, then start immediately upon data release.

@amirrmusavifr

Hi, I’m an AI expert with professional experience in computer vision, with a proven track record of working on complex image processing and AI/ML model development. With skill sets: • Algorithm Development: Strong understanding of computer vision algorithms and techniques, including convolutional neural networks (CNNs), object detection, image segmentation and feature extraction. • Model Training & fine-tuning: Develop and train machine learning models tailored for image analysis and visual data interpretation. I have worked on some well-known models like YOLO, RCNN, U-Net, Deeplab, ViT etc. • AI Integration: Implement and integrate AI models into existing software and hardware systems, ensuring high performance and scalability. • Data Analysis: Analyze and process large datasets of images and video feeds to identify patterns, trends, and insights. • Data Handling: Experience in handling and processing large datasets, including image and video data. Familiarity with data augmentation techniques and synthetic data generation. • Performance Optimization: Optimize algorithms and models for real-time processing and ensure they can handle large-scale data efficiently. • Programming Skills: Proficient in programming languages such as Python. Experience with deep learning frameworks like TensorFlow, PyTorch, or Keras. • Tools & Libraries: Proficiency with OpenCV, scikit-image, and other relevant libraries. Experience with version control systems like Git.

@lalitdeveloper7

Hi, I can develop a complete audio fraud detection system for classifying Phishing, Robocalls, and Telemarketing scams using Python, TensorFlow/PyTorch, Speech Processing, Deep Learning, CNNs, RNNs, Transformers, MFCCs, and Spectrogram-based features. The solution will include audio preprocessing, feature extraction, model training and optimization, performance evaluation using Precision, Recall, F1-Score, and Confusion Matrix, and an inference pipeline that accepts MP3 files and returns the predicted fraud category with a confidence score. I will provide the trained model, source code, model weights, documentation, and a deployable inference script or REST API. Please let me know further. Thanks.

@Marwan328

Hi, I can build this in PyTorch the pipeline would be: load MP3s with librosa, extract log-mel spectrograms (more robust than raw MFCCs for content + speaker cues), feed into a CNN classifier (or CNN+RNN hybrid if temporal patterns matter), trained with class-weighted loss to handle any imbalance across the three fraud types. Evaluation will cover per-class precision, recall, F1, and a full confusion matrix on a held-out test split. For deployment I'll wrap the inference in a lightweight FastAPI endpoint that accepts an MP3 and returns predicted class + confidence score, alongside all model weights and clean reproducible code. Could you share roughly how many recordings are in the corpus and whether they're labeled already, so I can plan the train/val/test split and estimate training time?

@Apponn

Hey there, I read your requirements and understand that you need a machine learning model to classify call recordings into Phishing, Robocalls, and Telemarketing scam categories with high accuracy. I have experience in machine learning, audio processing, and classification projects. I can handle the complete workflow, including feature extraction, model training, evaluation, and deployment with an inference script or API. I can provide the best solution for your project according to your budget and deliver clean, well-documented code. Thanks Rohit

@Yoyoqttt

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@Ashoksahu001

Hi, This project is fundamentally a speech-classification problem where success depends on both audio processing quality and model architecture. Rather than relying on a generic classifier, I would build a pipeline specifically optimized for fraud-call detection across your three target classes: Phishing, Robocalls, and Telemarketing Scams. Proposed Approach: ✔ Audio Preprocessing • Noise reduction & normalization • Silence trimming • MP3 ingestion pipeline • MFCC, Mel Spectrogram & Acoustic Feature Extraction ✔ Model Development • CNN + BiLSTM or Transformer-based architecture • TensorFlow or PyTorch implementation • Hyperparameter tuning • Class imbalance handling • Cross-validation & performance optimization ✔ Evaluation • Precision, Recall & F1 Score • Confusion Matrix • Per-Class Performance Metrics • ROC/AUC Analysis (if applicable) ✔ Deployment Deliverables • Trained Model Weights • Inference Script • REST API Service (FastAPI preferred) • Confidence Scores • Source Code & Documentation • Setup Instructions Tech Stack: ✔ Python ✔ PyTorch or TensorFlow ✔ Librosa ✔ NumPy/Pandas ✔ FastAPI ✔ Scikit-Learn Why I'm a strong fit? ✔ Machine Learning & Data Processing Experience ✔ Audio Classification Pipeline Development ✔ Python-Based Production Systems ✔ Model Evaluation & Deployment Expertise ✔ Clean, Documented Code Best Regards

@kanmani04

Hello, I am interested in developing your Audio Fraud Detection Model for identifying Phishing, Robocalls, and Telemarketing scam calls using deep learning and audio analytics. I have experience working with Python, machine learning pipelines, audio feature extraction, and classification models. For this project, I can build a complete end-to-end solution including: • Audio preprocessing (noise reduction, normalization, segmentation) • Feature extraction using MFCCs, Mel-spectrograms, and other speech features • Deep learning model development using CNN/RNN/Transformer-based architectures • Model training, tuning, and validation • Performance evaluation with precision, recall, F1-score, and confusion matrix • Deployment-ready inference script/API that accepts MP3 input and returns prediction with confidence score • Complete source code, trained weights, and documentation Proposed technology stack: Python, PyTorch/TensorFlow, Librosa, NumPy, Pandas, Scikit-learn, FastAPI/Flask for deployment. My approach will focus on building a reliable classifier that can generalize well on real-world call recordings rather than only achieving high training accuracy. I can provide milestone-based delivery with regular updates and testing reports. I am ready to start once the dataset and requirements are shared. Looking forward to working on this audio intelligence project. Thank you.

@Abhi928262829

Hello, I can build an end-to-end audio fraud detection system that automatically classifies call recordings into Phishing, Robocall, and Telemarketing categories. My approach will include: • Audio preprocessing and feature extraction using Librosa (MFCCs, Mel Spectrograms, spectral features). • Data cleaning and augmentation to improve model robustness. • Development of a deep learning classifier using PyTorch/TensorFlow (CNN, CRNN, or Transformer-based architecture depending on dataset size). • Comprehensive evaluation including Precision, Recall, F1 Score, and Confusion Matrix. • Deployment-ready inference script or lightweight REST API that accepts MP3 files and returns predicted class and confidence score. • Delivery of complete source code, trained model weights, and documentation. Recommended toolkit: Python, PyTorch, Librosa, NumPy, Scikit-learn, FastAPI, and Pandas. Before training, I would like to review the dataset size, class distribution, and average recording length to determine the optimal architecture and expected accuracy. Estimated delivery: 5–7 days after receiving the dataset.

@niveditajha001

Hello , I am a co-founder of an Artificial intelligent software startup that works on face recognition, speech recognition, machine learning and other AI stuff . I have worked on various pattern recognition and speech recognition projects . I hope I will be able to help you with the project .

@shazimjaved

✔ I deliver 100% work — 99.9% is not for me. ✔ Workflow Diagram: Audio Analysis ⟶⟶ Data Preprocessing ⟶⟶ Feature Extraction (MFCC/Spectrograms) ⟶⟶ Model Training & Tuning ⟶⟶ Evaluation & Validation ⟶⟶ Inference API Development ⟶⟶ Testing & Delivery Key Highlights ✔ Strong experience with Python, PyTorch, TensorFlow, audio processing, deep learning, and classification systems. ✔ Audio preprocessing pipeline including noise reduction, normalization, feature extraction, and dataset preparation. ✔ Development of CNN, RNN, Transformer, or hybrid architectures based on dataset characteristics and accuracy targets. ✔ Detailed evaluation using Precision, Recall, F1-Score, confusion matrices, and class-wise performance analysis. ✔ Deliver a ready-to-use inference script or REST API that accepts MP3 files and returns predicted fraud categories with confidence scores. ✔ Complete source code, trained model weights, documentation, and reproducible training pipeline included. ✔ Focus on building a scalable solution capable of accurately identifying Phishing, Robocalls, and Telemarketing scam calls. Best Regards, Shazim Machine Learning Engineer | Audio Processing Specialist | Python & Deep Learning Expert

@rashedu7

Hello, I am excited to apply for your project involving automatic detection of fraudulent call recordings. I understand you need a machine learning system that can accurately classify audio into Phishing, Robocalls, and Telemarketing scams, along with a deployable solution that works without human intervention. I specialize in machine learning, deep learning, and audio signal processing, and I can deliver a robust, production-ready system tailored to your requirements. I am ready to start immediately and can also suggest improvements to maximize model performance. Looking forward to working with you. Best regards, Rashed Ul Islam Machine Learning & AI Engineer

@SavajInsights

Hi, I've worked on audio classification pipelines before, so I know the real challenge here isn't the model — it's the data. Phishing calls, robocalls, and telemarketing scams can sound surprisingly similar at the audio level, so the architecture choice really depends on what your recordings actually look like. My instinct would be to start with a CNN on Mel spectrograms as a baseline, then layer in a BiLSTM or Transformer if temporal patterns matter (they usually do for robocalls, which tend to have very specific speech rhythms). I'd use Librosa for preprocessing, PyTorch for the model, and wrap inference in a FastAPI endpoint that takes an MP3 and returns the class + confidence score. I'll deliver everything you asked for, preprocessing scripts, trained weights, precision/recall/F1 and confusion matrix per class, and clean documented code you can maintain yourself. Before I go further though, how many recordings are we working with, and are they already labelled? That'll determine whether we need to think about class imbalance handling or semi-supervised approaches upfront, which changes the timeline quite a bit. Happy to discuss architecture before any code is written.

@abhinavv13

This sounds like an interesting project. I've worked with audio classification and ML pipelines before, and I can help build a model that identifies phishing, robocalls, and telemarketing calls from MP3 recordings. I'd likely start with MFCC/spectrogram features and test a few architectures to see what performs best on your dataset. I'll provide training code, evaluation metrics, model weights, and an API for inference. Once I can review a few sample recordings and understand the dataset size, I can suggest the most practical approach and timeline.

@usamaKhan577

Hi, You need MP3 call recordings classified into Phishing, Robocalls, and Telemarketing automatically — with clean preprocessing, a solid trained model, per-class metrics, and a working inference script that returns a prediction and confidence score. I have read everything carefully. I would use librosa for audio cleaning and MFCC plus log-mel spectrogram extraction, then train a CNN baseline first and move to a CNN-RNN hybrid if temporal patterns improve fraud separation, all in PyTorch, modular and retrain-ready. You get trained weights, clean documented code, precision, recall, F1 and confusion matrix per fraud type, and a REST inference script accepting MP3 input. I am still building my Freelancer profile so I will not pretend I have ten similar deployments behind me. What I will do is treat this as my flagship project and over-deliver on every metric we agree on. One question, how variable is the call length and audio quality across your dataset? That shapes the preprocessing pipeline more than anything else. Regards, Usama Ahmad

@sanjeevmlai

Your audio fraud detection project needs exactly the ML pipeline I build professionally. I'll extract MFCC and spectrogram features from your MP3 files, train a CNN-RNN hybrid classifier to distinguish Phishing, Robocalls and Telemarketing scams, and deliver full evaluation metrics — precision, recall, F1 and confusion matrix per class. Deliverables: trained model weights + inference REST API that accepts MP3 and returns predicted class with confidence score. Stack: Python, PyTorch, Librosa. I built fraud detection ML systems in production at Virtusa for banking clients and hold an M.Sc. in Mathematics from IIT Bombay. Can start immediately once audio data is shared. What is the size of your corpus?

@yashvardhana1

Hello, I can develop an end-to-end machine learning solution to automatically classify call recordings into Phishing, Robocalls, and Telemarketing Scams. The project will include audio preprocessing, feature extraction, model training and fine-tuning, performance evaluation (Precision, Recall, F1-Score, and Confusion Matrix), and a deployment-ready inference script or REST API that accepts MP3 files and returns the predicted class with a confidence score. Project Delivery: 4 Days Fixed Price: Rs.1,351 I will provide the complete source code, trained model weights, documentation, and deployment assets. Looking forward to working with you. Thank you.

@shumails45

Hello, I am a BS AI student with experience in Machine Learning, Python, K-Means, DBSCAN, and Data Analysis. I have completed similar projects and can deliver quality work within the deadline. Looking forward to discussing your project.

@marios050

Hello, I am a Data Science student with experience in Python, machine learning, deep learning, and data preprocessing. I am interested in building a fraud-call classification system capable of identifying Phishing, Robocalls, and Telemarketing scams from audio recordings. My proposed workflow includes: • Audio preprocessing and cleaning • Feature extraction using MFCCs, Mel Spectrograms, and other relevant audio features • Training and evaluating multiple deep learning models (CNN, RNN/LSTM, or hybrid architectures) • Hyperparameter tuning and model optimization • Performance evaluation using Precision, Recall, F1-Score, and Confusion Matrix • Confidence-based predictions for each audio file Deliverables: • Fully documented Python code • Trained model and saved weights • Reproducible training pipeline • Evaluation report with performance metrics • Inference script or REST API for MP3 classification • README with setup and usage instructions Preferred tools: • Python • PyTorch or TensorFlow • Librosa for audio processing • Scikit-learn for evaluation and analysis I focus on creating reproducible and well-documented machine learning solutions that can be easily maintained and extended. I would be happy to review the dataset and discuss the best architecture and timeline for achieving reliable fraud detection performance. Best regards, Mario Sameh Data Science Student | Machine Learning & Deep Learning