High-Accuracy Multimodal Cry Classifier

@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

@nrajuu07

As a data scientist with a specialty in deep learning, I believe I'm the perfect fit for your project. My industry experience includes work on complex multimodal perception problems, similar to your cry classification task. In fact, I’ve successfully tackled cry detection, sound-event recognition, and emotion analysis. Additionally, my proficiency with neural networks, especially CNNs and RNNs, will be instrumental in fusing and training audio and video inputs within a single architecture. I'm well-versed in various deep-learning stacks like PyTorch and TensorFlow—arguably some of the best tools for this job. What's more, I am experienced in deploying models to edge devices and exporting them to formats like ONNX or TFLite. This offers two crucial benefits for your project: real-time processing on-demand and easy integration into your final product. Furthermore, having worked within biomedical data science domains such as CT scans and cancer detection, I'm familiar with handling sensitive data. You can trust that your proprietary dataset will remain secure but also effectively augmented through comprehensive public sources. My training pipeline is robust and well-documented while my evaluation reports provide precise insights through confusion matrices and detailed per-class metrics—perfect for meeting your stringent project demands. Allow me to exceed your expectations with my expertise!

@quan9824

I have hands-on experience building multimodal audio-visual classifiers for edge deployment, including sound-event recognition and behavior-state inference pipelines where audio and video are fused in a single network (not late independent voting). I typically use PyTorch + torchaudio + OpenCV, with temporal fusion via cross-modal attention or lightweight transformer heads, then optimize for edge inference and export to ONNX/TFLite with INT8 quantization when needed. Relevant work includes: (1) real-time acoustic event detection pipelines with noisy-field robustness, (2) video+audio behavior classification prototypes with synchronized clip sampling and temporal augmentation, (3) production inference services packaged for low-latency deployment. I can share code samples and implementation structure (training/eval/export/inference) aligned to your deliverables: documented training pipeline, trained checkpoints, ONNX/TFLite exports, integration-ready inference service, and full evaluation reporting (confusion matrix + per-class precision/recall + methodology). My workflow is metric-driven with strict hold-out validation and cross-validation to meet target reliability before handoff.

@devanshkhandekar

Hi, I’m an Applied ML Engineer specialised in building edge-ready multimodal models and ships them as ONNX/TFLite . My approach for your project would be (true fusion): Data + labeling: segment synced AV windows (eg: 2s / 0.5s stride), enforce subject-wise splits (no baby leakage), and unify targets as Cry/No-cry + {No pain, Moderate, Pain} Relabel open audio: pretrain audio encoder on public cry-reason datasets, then map into your taxonomy via a defensible mapping table + weak-supervision confidence (pain -> Pain, discomfort ->Moderate, non-cry->No-cry) to boost robustness Fusion architecture: lightweight audio CRNN/DS-CNN + tiny video backbone + mid-level fusion (cross-attention / gated fusion) -> shared embedding multitask heads (cry + pain) Generalization + metrics: heavy real world aug (noise, low-light/blur), rigorous CV, confusion matrix + per-class P/R/F1, and an uncertain threshold option to keep precision high. Edge deployment: export ONNX/TFLite, INT8 quantization (QAT/PTQ), latency profiling, and a clean inference microservice/API. Relevant shipped work: On-device audio event detector : streaming log-mel pipeline, smoothing/cooldown logic, INT8 quantization, real time mobile inference. Multimodal perception for safety monitoring: audio+video temporal fusion with gating for noisy/low-visibility conditions, subject-independent evaluation. Edge CV pipeline: optimized detection/classification with ONNX Runtime/TensorRT-style speedups and reproducible training+export.

@lalitdeveloper7

Hi I can develop a multimodal deep-learning model that fuses baby cry audio and paired video within a single architecture, optimised for real-time edge deployment and exportable to ONNX or TFLite. The solution will include a documented training pipeline, cross-validated evaluation exceeding your precision/recall targets, trained model files, inference script, and a clear integration guide for product use. Please let me know further. Thanks

@Web1devloper

With my extensive 8+ years of experience in Data Analytics and Science, I bring to the table a unique blend of skills for your High-Accuracy Multimodal Cry Classifier project. Not only do I have an excellent understanding of Python and its various scientific libraries, including TensorFlow and PyTorch, but I also possess deep expertise in handling complex datasets and building predictive analytics models from scratch. This aligns perfectly with your needs for creating a production-ready model that integrates audio and video through a streamlined deep-learning architecture. I understand the importance of accuracy goals in this project, especially when it involves classifying different emotions based on sound and video cues with high precision. To demonstrate my capabilities, I encourage you to explore my GitHub repository (link here) where you can see my previous work on sound-event recognition and emotion analysis – two domains that closely relate to your project at hand.

@kend27

I’m a senior AI/ML engineer with experience building production-ready multimodal systems combining audio and video for real-time inference. I’ve developed and deployed streaming voice bots (LLM + ASR + TTS), sound-event detection pipelines using torchaudio and PyTorch, and computer-vision systems using OpenCV and deep CNN/Transformer architectures. My work includes real-time inference optimization, ONNX export, edge deployment, and robust evaluation with cross-validation and per-class metrics. I’ve built end-to-end training pipelines with augmentation, modality fusion (early/late/cross-attention), and deployment-ready microservices. Relevant work samples and repositories can be shared upon request.

@Hemantt841

With 7 years of experience in the field, I am the best fit to complete this project. I have the relevant skills and have worked on similar solutions in the past. How I will complete this project: - I will build a production-ready model that listens to a baby’s cry, watches the paired video, and reliably classifies the cause. - I will fuse audio and video inside one architecture to achieve high accuracy goals. - The final network will run in real-time on an edge device and be exported to ONNX or TFLite format. Tech stack I will use: - I will utilize deep-learning frameworks such as PyTorch, TensorFlow, Keras, OpenCV, and torchaudio. - I will expand the proprietary dataset through public sources or augmentation. - Rigorous cross-validation will be performed to refine the model until it consistently exceeds 90% precision and recall on an unseen hold-out set. Deliverables: - Well-documented training pipeline and source code. - Trained model file(s) with lightweight export to ONNX/TFLite. - Inference script or microservice ready for product integration. - Evaluation report including confusion matrix, per-class metrics, and brief methodology. - Integration guide detailing inputs, outputs, and runtime footprint. I have the expertise and experience to deliver a high-accuracy multimodal cry classifier that meets your requirements.

@phpxpert89

Hello Deepak S., I checked your project, and it looks interesting. This is something we already work on, so the requirements are clear from the start. We mainly work on Python, Data Processing, Algorithm, Data Science, Keras, Computer Vision, Deep Learning, Natural Language Processing We focus on making things simple, reliable, and actually useful in real life not overcomplicated stuff. Let’s connect in chat and see if we’re a good fit for this. Best Regards, Ali nawaz

@keaganmini

I am an excellent fit for your project, having successfully completed similar work in the past. I understand you need a seamless, integrated audio-video model to classify baby cries by cause with over 90 percent precision and recall, running efficiently on edge devices. My expertise includes deep learning frameworks like PyTorch and TensorFlow, along with experience in sound-event recognition and multimodal fusion. Even though I am new here, I have worked on numerous projects outside of freelancer and developed the skills necessary to complete this work effectively. I’d be glad to discuss your project—at best, we find a strong fit to work together; at minimum, you receive a complimentary consultation. Regards, Keagan

@bjorkb

I have just completed a similar project. I developed a multimodal deep learning system that seamlessly fuses audio and video streams within a unified architecture, reaching over 92% precision and recall on real-time edge devices. You won’t find a specialist better aligned with what you’re looking for. I understand the importance of achieving reliable, high-accuracy classification in a production-ready, resource-constrained environment. I specialize in transforming complex business requirements into high-converting, user-centric digital assets. I’d love to chat about your project! The worst that can happen is you walk away with a free consultation. Regards, Bjork Bronkhorst