Audio-Physio Prediction 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

@nabeelraza1993

Building a reproducible multimodal unsupervised classifier that fuses audio and physiological signals and still generalizes to new subjects is not a problem with the right feature pipeline, fusion architecture, and validation protocol. Well, what I can do for you as an engineer with strong AI and machine learning background is refine your preprocessing for waveforms and ECG EDA respiration, design a clean multimodal model in PyTorch or TensorFlow, implement evidence driven hyperparameter search and cross validation, and deliver training and inference code that targets beating your current baseline F1. In fact, I have worked on ML oriented research and technical writing where reproducibility depends on strict data splits, leakage control, and transparent evaluation, so I will structure the codebase with deterministic runs, clear configs, and logged metrics so every result can be reproduced and defended.

@Bhartifreelancer

I may not have an immediate tie to data analysis or the world of deep-learning, but my adaptability and passion for learning ensure that I'll excel in any challenge. Over the years, my adeptness with technical skills, problem-solving ability, and proficiency in multiple programming languages has allowed me to transition effortlessly between different domains. It's this characteristic that makes me uniquely qualified for your Audio-Physio Prediction Model project. Your project demands a diligent, skilled professional capable of staying ahead of the curve in an ever-evolving field like deep learning. I have consistently proven my ability to grasp complex concepts quickly and translate them into effective solutions through clear lines of communication. As an expert in Python and TensorFlow/PyTorch, delivering a robust prediction model won't just be a job for me; it'll be a commitment to surpass your current baseline F1.

@chiragardeshna

Hi there, I am a strong fit because I have built multimodal deep-learning models combining time-series biosignals and audio, with reproducible training pipelines and evidence-driven evaluation. I have implemented CNN-RNN and transformer-based fusion architectures in PyTorch, designed synchronized preprocessing for ECG, EDA, respiration, and waveform features, and delivered cross-validated models with detailed metric reporting. For this project, I would formalize the preprocessing pipeline, align modalities with consistent windowing, benchmark late vs. early fusion architectures, and implement stratified cross-validation to maximize subject-level generalization. I reduce risk by enforcing strict train-test subject separation, logging experiments with reproducible configs, tracking F1 and confusion matrices per fold, and delivering modular code with a clear inference wrapper for new recordings. I am ready to review your baseline notebook and propose an architecture refinement plan with milestones once data specs and current metrics are shared. Regards Chirag

@devanshkhandekar

Hi,I am a seasoned Applied AI Engineer with experince of more than 6 years.I can help you turn your synced audio + ECG/EDA/resp streams into a clean, reproducible multimodal pipeline + model that generalizes to new subjects Approach Leakage-safe evaluation: enforce subject-wise splits (or session-wise), plus k-fold CV for reproducible results Preprocessing & alignment: robust resampling + timestamp alignment, bandpass/filtering for ECG/EDA/resp, artifact handling, windowing strategy (5–10s windows with overlap) & normalization (per subject vs global tested) Feature pipelines: Audio: log-mel spectrograms / pretrained audio embeddings Physio: time+freq features (HRV for ECG, tonic/phasic for EDA, respiration rate) and/or 1D CNN features learned Fusion architecture: start with a robust baseline (dual-branch 1D/2D CNN + late fusion), then test CNN-RNN or Transformer cross-attention fusion if it improves F1 Training: class imbalance handling, calibration, early stopping, hyperparam search & detailed metrics (per-class precision/recall/F1 + confusion matrix) Deliverables: modular PyTorch codebase, training scripts, saved weights,inference wrapper for new audio+physio files and returns probabilities Relevant experience: Built multimodal time-series models combining audio with sensor/biometric signals (windowing, sync, robust CV) for subject-generalization Shipped production pipelines with reproducible training, ablation reports, and clean inference APIs for real-world deployment

@Hemantt841

I was immediately drawn to your Audio-Physio Prediction Model project description. The unique challenge of building an unsupervised classifier that learns from both audio recordings and physiological signals is fascinating. With over 7 years of experience in software development, I believe I have the expertise to tackle this project effectively. Here's how I would approach completing this project: - Conduct thorough data preprocessing to ensure compatibility between audio and physiological signals - Implement a hybrid CNN-RNN or transformer fusion architecture for optimal model performance - Utilize TensorFlow for seamless integration of deep learning algorithms - Develop training scripts for efficient model training and testing - Deliver a clean, well-documented codebase for easy maintenance and future improvements In a recent project, I successfully developed a similar prediction model that integrated audio and visual data to predict user behavior with an impressive accuracy rate of 90%. This experience makes me confident in my ability to deliver results that meet or exceed your expectations. As I delve into this project, I'm curious about how you envision incorporating real-time data streams into the model for continuous learning and adaptation. Your insights will help me tailor the implementation to best suit your needs. Feel free to review my portfolio for relevant wo

@Techinfomatic

Hello client, Hope you are doing good!!! I recently Reached across your job post that you are looking for a skilled developer to building a unsupervised classifier that learns jointly from audio recordings and accompanying physiological signals and as per your scope of work and required technology i felt that I can assist you perfectly. I have 8+ years of working experience as a website developer & Mobile developer as well. I have vast experience with all the tech stack you have mentioned Feel free regarding any clarification. I am ready to start immediately and looking forward to long business association ahead. Thanks and Looking Forward Rasna Rajput

@AryaJoshi21

Hi, I’m Arya Joshi, an AI/ML engineer experienced in multimodal deep learning, audio processing, and physiological signal modelling. I’ve built end-to-end speech/audio models before, including a Mel-spectrogram deep learning system for fake audio detection with ~90% accuracy. For your project, I can: • Build a clean, reproducible preprocessing pipeline for audio + ECG/EDA/respiration • Design a multimodal CNN-RNN / Transformer fusion model • Implement subject-wise cross-validation and hyperparameter tuning • Deliver full metrics (Accuracy, Precision, Recall, F1, Confusion Matrix) • Provide modular training code, saved model, and inference script

@manuj1808

Hello, This is exactly my kind of ML problem — multimodal signal learning with reproducible deep-learning pipelines. I can take your synchronized audio + physiological data (ECG, EDA, respiration) and build a clean, evidence-driven training framework in PyTorch or TensorFlow that improves on your current F1 baseline and generalizes across subjects. My approach: • Robust preprocessing pipeline (signal sync, windowing, denoising, feature stacks + learned embeddings) • Multimodal fusion architecture (CNN/Transformer for audio + temporal encoder for physio, with late or cross-attention fusion) • Proper subject-aware cross-validation and leakage-safe splits • Structured hyperparameter search + ablation checks • Fully reproducible training scripts with fixed seeds and config files Deliverables: • End-to-end, well-commented training code • Saved model + inference script (audio + physio → class probabilities) • Evaluation report with accuracy, precision, recall, F1, confusion matrix • Notes on improvement paths I’ve worked with signal + deep learning pipelines and focus on modular, research-clean implementations. Happy to review your baseline notebook and propose the exact architecture.

@gsinfotechopcp4

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@priyankas716

Hi Dear, I am Priyanka I specialize in Python-based deep learning, multimodal signal processing, and reproducible ML workflows. I can deliver a clean end-to-end pipeline using TensorFlow or PyTorch, including preprocessing of audio and physiological signals, model design (CNN-RNN or transformer fusion), cross-validation, and performance reporting. My focus is on modular, well-documented code with training scripts, inference wrapper, and clear guidance to ensure your classifier generalises robustly to new subjects. Thank You Priyanka Singh