Audio Fraud Detection Model

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@glanegonsalves

Hey there Glane here, so once the audio is shared, I'll clean and standardize the MP3 call recordings, extract key speech features, and train a model to automatically classify calls as phishing, robocalls, or telemarketing scams. I’ll evaluate performance using precision, recall, F1 scores, and confusion matrices, then deliver a simple inference service that takes an MP3 file and returns the predicted class with a confidence score, along with all model weights and code for deployment.

@devanshkhandekar

Hi,I am a seasoned Applied ML Engineer with experience of more than 6 years. I can build an end-to-end speech/audio fraud classifier for MP3 call recordings that flags Phishing, Robocalls &Telemarketing scams with clear metrics & a deployable inference service. Approach Data audit & labeling: check class balance, call durations, sampling rates, noise profiles; define train/val/test split by caller/campaign Pre-processing: MP3 decode -> resample, loudness normalization, VAD to remove long silences, noise reduction, chunk long calls into fixed windows with aggregation Feature strategy: Baseline: log-mel spectrogram + MFCC deltas Upgrade: pretrained audio embeddings (wav2vec2/HuBERT style features) + classifier head for better content cues. Modeling: start with a strong baseline (CNN/CRNN) then move to Transformer/Conformer-lite if it improves F1; handle class imbalance with focal loss / weighted CE Evaluation: per-class precision/recall/F1, confusion matrix, threshold calibration (operating points for flag vs review) & error analysis on false positives Deployment: clean inference script + FastAPI endpoint: upload MP3 -> returns {class, confidence}; packaged weights & reproducible training Relevant experience: Built call/audio analytics pipelines: VAD segmentation, speaker/content feature extraction, and production inference services. Delivered multi-class audio event classifiers with strict evaluation, leakage-safe splits, and confidence calibration for real-world triage.

@chiragardeshna

Hi there, I am a strong fit because I have built speech-classification and audio-analytics models using CNN and transformer-based architectures for fraud and intent detection tasks. I have implemented full pipelines in PyTorch and TensorFlow, including audio cleaning, MFCC and log-mel spectrogram extraction, fine-tuning pretrained models such as wav2vec, and delivering structured evaluation reports with precision, recall, F1, and confusion matrices. For this project, I would benchmark a log-mel CNN baseline against a fine-tuned wav2vec or HuBERT transformer model, apply speaker-normalized preprocessing, enforce stratified train-test splits, and track per-class performance for Phishing, Robocalls, and Telemarketing scams. I reduce risk by validating data quality early, preventing data leakage across callers, performing cross-validation, logging experiments reproducibly, and delivering a clean inference script or lightweight FastAPI service with saved weights. I am ready to review dataset size, class balance, and baseline metrics so I can propose a phased timeline and architecture plan before training begins. Regards Chirag

@saranyakuduvan

Hi, I saw your request for audio call fraud detection. Kindly share your dataset with me. I will check if the dataset is balanced or imbalanced. If it's imbalanced, we augment the dataset to balance it. As expected, we will use MFCCs or spectrograms for feature extraction. After that, we will use a hybrid classification model. I will provide all possible result parameters, such as accuracy, precision, recall, F1 score, ROC curve, and confusion matrix. I am a professional MATLAB & Python developer and a journal paper writer, especially for PhD scholars and master students. Kindly text me we will discuss more. Thanks

@Hemantt841

I was immediately drawn to your project description for the Audio Fraud Detection Model, as it aligns perfectly with my expertise in machine learning and speech analytics. With over 7 years of experience in software development, I have successfully completed projects that required complex data processing and accurate classification models. To tackle this project effectively, I would approach it in the following key steps: - Pre-process the raw call recordings to extract relevant features using MFCCs or spectrograms. - Design and train a classifier using advanced techniques like CNN, RNN, or a hybrid approach to improve accuracy. - Evaluate the model performance by providing precision, recall, F1 scores, and a detailed confusion matrix for each fraud type. - Develop deployment assets such as an inference script or a REST service in Python with TensorFlow or PyTorch for seamless integration. In a recent project similar to yours, I developed a speech recognition system that accurately identified different speakers in a conversation, achieving a 95% accuracy rate. I utilized a combination of MFCCs and CNNs to achieve this result, showcasing my ability to deliver precise and reliable solutions. I am excited about the opportunity to work on this project and would like to discuss further details. Could you please clarify if there are any specific requirements or constraints that ne

@DigitalNightOwl8

Hello, Greetings , Good afternoon! I’ve carefully checked your requirements and really interested in this job. I’m full stack node.js developer working at large-scale apps as a lead developer with U.S. and European teams. I’m offering best quality and highest performance at lowest price. I can complete your project on time and your will experience great satisfaction with me. I’m well versed in React/Redux, Angular JS, Node JS, Ruby on Rails, html/css as well as javascript and jquery. I have rich experienced in Audio Processing, Python, Recurrent Neural Network, Machine Learning (ML), Convolutional Neural Network, Big Data Sales, Algorithm and Deep Learning. For more information about me, please refer to my portfolios. I’m ready to discuss your project and start immediately. Looking forward to hearing you back and discussing all details.. Have a great time

@AryaJoshi21

Hi, I’m Arya Joshi, an AI/ML engineer with experience in speech analytics and audio deep learning. I’ve previously built a Mel-spectrogram based model for detecting fake audio (GAN-generated speech) that achieved ~90% accuracy, handling full preprocessing, feature extraction, training, and evaluation. For your project, I can build an end-to-end system that: • Cleans audio and extracts MFCC/Mel-spectrogram features • Trains a CNN/RNN/Transformer-based classifier for Phishing, Robocalls, and Telemarketing • Provides Precision, Recall, F1, and confusion matrix • Delivers a Python inference script / REST API that takes MP3 and returns predicted class + confidence I can handle the complete pipeline from preprocessing to deployment. Once you share the dataset, I can start immediately.

@kashishgoell

Hello, I’m an AI/ML developer with hands-on experience building deep learning models using PyTorch and TensorFlow, including neural network design, evaluation pipelines, and deployment-ready inference systems. For your fraud call classification system, I would: • Preprocess audio using Librosa (noise reduction, normalization, resampling) • Extract MFCCs and Mel-spectrogram features • Train a CNN or CNN+BiLSTM model to capture both audio patterns and temporal speech behavior • Optimize using precision, recall, F1-score, and confusion matrix per class • Deliver a complete inference script or FastAPI REST service that accepts MP3 input and returns predicted class with confidence score I focus on building clean, reproducible ML pipelines with proper evaluation and deployment assets — not just training a model. If you share dataset size and sample duration, I can finalize architecture and timeline immediately. Looking forward to working with you.

@PyMLExpert1

With a portfolio bursting at the seams with successful machine learning deployments, I am confident in my ability to deliver on each and every aspect of your project requirement. Having previously created robust audio analytics models, my experience sets me apart. I have tackled problems like yours head-on, using tools such as TensorFlow, PyTorch and more. My approach is meticulous. Beginning with pre-processing, I will ensure audio quality through cleansing techniques while simultaneously extracting MFCC and spectrogram features that capture speaker and content cues. This strong foundation will lay the groundwork for a hybrid CNN, RNN or Transformer model using my deep understanding of these architectures to improve accuracy on fraud identification. To gauge real-world performance, rest assured that I will provide detailed evaluation metrics including precision, recall, F1 scores, and a full confusion matrix for each fraud type. This comprehensive approach ensures complete coverage and meets your project's objective fully.I will further provide clear deployment assets to help you continue using the model effectively. By offering not only my skills in deep learning and Python but also my holistic comprehension of your project's needs, I am confident I can exceed expectations and deliver an efficient audio fraud detection model for you. Award me this project and let's get started transforming your call recordings to exceptional results.

@Jeremys02

EXPERT ((Python, Recurrent Neural Network, Deep Learning, Machine Learning (ML), Big Data Sales, Convolutional Neural Network, Audio Processing and Algorithm)) DEAR EMPLOYER, I’ve completed the exact same projects before successfully. Awarding me will be the fastest way to complete your task with the best rates possible. I CAN ASSURE YOU 100% THAT WE ARE FULLY CAPABLE OF EXECUTING ANY LEVEL OF TASK/PROJECT BASED ON THE SKILL REQUIRED. I am fully confident about our skills and my understanding of the project description and we are ready to go through any test or sample task you assign to acquire your trust. Let me know when are you available for an initial 15-30-minute discussion (FREE OF CHARGE) so we can discuss the requirement in detail and I can walk you through the mentioned systems to acquire your trust in my skill. REST ASSURED YOUR WORK IS IN VERY SAFE AND PROFESSIONAL HANDS. THANK YOU

@gsinfotechopcp4

GSINFOTECH OPC Pvt. Ltd. – Your Trusted Tech Partner Based in New Delhi, GSINFOTECH OPC Pvt. Ltd. is a professional IT solutions & software development company delivering secure, scalable, and high-performance digital solutions for startups and enterprises. We help businesses convert ideas into powerful, market-ready products. Our Services •⁠ ⁠Mobile App Development (Android & iOS) •⁠ ⁠Desktop Software Development (C#, Java, .NET) •⁠ ⁠Custom Software & Web Application Development •⁠ ⁠Website Design & Development (WordPress, Joomla, Drupal) •⁠ ⁠Laravel, React JS & Node JS Development •⁠ ⁠Game Design & Development •⁠ ⁠Blockchain Solutions •⁠ ⁠AI, Automation & Custom Tools •⁠ ⁠Meta Trading Tools, Bot Scripting & Web Scraping •⁠ ⁠SEO, Digital Marketing & Branding •⁠ ⁠Video Editing & Multimedia Production Technologies We Use •⁠ ⁠React JS, Node JS, MongoDB •⁠ ⁠Python (Django) •⁠ ⁠Android Studio (Java/Kotlin), iOS (Swift) •⁠ ⁠Flutter & React Native Why Choose Us? ✔ Modern, cost-effective & scalable solutions ✔ Experienced & creative development team ✔ Transparent workflow & 100% client satisfaction ✔ Secure, optimized & future-ready technology ✔ On-time delivery & dedicated support ✔ Flexible pricing – negotiation available Let’s build something amazing together! Hire GSINFOTECH OPC Pvt. Ltd. to take your project to the next level.

@mingl48

Hi — I can build this fraud detection classifier using a CNN + attention architecture over Mel spectrograms extracted via librosa. Pipeline: MP3 → audio preprocessing → MFCC/Mel spectrogram features → trained classifier with separate heads for Phishing, Robocalls, and Telemarketing. Deliverables: • Trained model with full evaluation (precision, recall, F1, confusion matrix per fraud type) • Inference REST API (FastAPI) accepting MP3, returning predicted class + confidence score • All model weights, training code, and notebooks Stack: Python, PyTorch, librosa, FastAPI. I have production experience building audio classification systems and can start immediately once the corpus is shared. Happy to discuss architecture choices and milestones.

@ahmadkhan24

Hello, I carefully reviewed your project and understand that you need an end-to-end machine learning solution to analyze raw MP3 call recordings and automatically classify them as Phishing, Robocalls, or Telemarketing scams without human intervention. I can develop a complete pipeline covering audio preprocessing (noise reduction, trimming, normalization), feature extraction (MFCCs / log-Mel spectrograms), model development using CNN/RNN/Transformer or a hybrid approach, and rigorous evaluation with precision, recall, F1 score, and confusion matrix for each category. What You Will Get: You will receive a production-ready deliverable including trained model weights, clean Python code (PyTorch or TensorFlow), and an inference script or lightweight REST service that accepts an MP3 file and returns the predicted class with a confidence score. The system will be designed to handle real-world audio challenges such as noise, varying call durations, and possible class imbalance. To ensure the best results, I would appreciate clarification on: Approximate number of recordings and average duration Whether the data is already labeled Single-speaker or multi-speaker calls Real-time processing requirement or batch processing Any preferred framework (TensorFlow or PyTorch) I am ready to begin immediately once details and milestones are finalized, and I will keep communication clear throughout the project. Best regards, Ahmad

@deepakj146

Hi there, I am excited to bid on your project for developing an automated Audio Fraud Detection Model. I have extensive experience in building custom AI solutions using Python, TensorFlow, and PyTorch, specifically focusing on signal processing and speech analytics. My Proposed Technical Approach: Pre-processing: I will implement a robust pipeline to clean raw MP3 files and extract high-fidelity features like MFCCs, Mel-Spectrograms, and Chromagrams to capture both tonal and content cues. Modeling: I suggest a Hybrid CNN-LSTM or Transformer-based architecture. This ensures we capture both the spatial features of the audio "image" and the temporal sequence of the speech to accurately distinguish between Phishing, Robocalls, and Telemarketing scams. Evaluation: I will provide a comprehensive report including Precision, Recall, F1-Score, and a detailed Confusion Matrix for each category to ensure the model performs reliably in real-world scenarios. Deployment: I will deliver a production-ready REST API (FastAPI/Flask) that accepts MP3 files and returns the predicted class with a confidence score. Why Choose Me? I have worked on similar speech analytics projects where I dealt with noisy datasets and high-accuracy requirements. My toolkit includes Librosa for audio analysis and Scikit-learn/TensorFlow for robust classification. I am ready to discuss the architecture and milestones so we can get started with the corpus immediately. Best regards, Deepak joshi

@Arthurshree

Hello! I’m a Machine Learning enthusiast with strong Python fundamentals and hands-on experience working with data processing and deep learning concepts. While I may not have many large freelance projects yet, I am highly dedicated and confident in building the audio fraud detection pipeline you described — including preprocessing (MFCC/spectrogram extraction), model training, evaluation metrics, and an inference script that predicts fraud type with confidence. I focus on clean, understandable code and clear communication throughout the project. Additionally, I am also a Flutter developer, so in the future I can help integrate this trained model into a mobile app if you decide to deploy it. I’m eager to take responsibility for this project and deliver a reliable working solution that meets your requirements.