AI Snore Pattern Analyzer Dashboard

@ZohaibRoy

⭐⭐⭐⭐⭐ Full-Stack Engineer for Audio Signal Processing & Web Dashboard ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you are looking for a full-stack engineer. You have no need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in audio processing and web development. I will create a robust AI/DSP pipeline to analyze recordings and present insights in a user-friendly dashboard. ➡️ Why Me? I can easily tackle your audio signal processing project as I have 5 years of experience in full-stack development, specializing in audio analysis, data visualization, and web applications. My skills include Python programming, real-time data processing, and UI design. I also have a strong grip on React and various machine learning frameworks, ensuring a complete solution for your needs. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I look forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ Full-Stack Development ✅ Audio Signal Processing ✅ Python Programming ✅ React.js ✅ Data Visualization ✅ AI & Machine Learning ✅ API Development ✅ Web Dashboard Design ✅ Real-Time Data Processing ✅ User Interface Design ✅ Model Validation ✅ Documentation Writing Waiting for your response! Best Regards, Zohaib

@Herculesz

Hi And I can help you so on by stepping into your Electron project and pushing it toward a stable, production-ready desktop application. I have strong experience with Electron, TypeScript, React, and Node.js, including handling tray systems, global shortcuts, and audio-based workflows. At this stage, a common issue is instability caused by partially integrated features like SSO, subscriptions, and native OS behaviors. I solve this by properly structuring communication between main and renderer processes, implementing secure Google OAuth, and ensuring consistent backend sync for subscriptions. I can also handle release preparation including packaging, installers, code signing, and macOS notarization with clean environment handling. Additionally, I improve logging, crash reporting, and debugging to resolve issues around hotkeys, window focus, and audio handling. My focus is to stabilize your existing architecture and ensure consistent performance across macOS and Windows environments. Best Regards, Hercules

@kamran2012

Hi there, I will build your snore pattern analyzer — detection pipeline, REST API with per-second labels and WOB metrics, and the monitoring dashboard with circular gauges, animated sphere, and timeline with clip playback. For slow-snore detection, I will use a mel-spectrogram approach with a lightweight CNN trained on labeled segments, since slow snores sit in a narrow frequency band that standard energy thresholds miss. A confusion-matrix validation routine will ship alongside the model so you can verify accuracy per class. Questions: 1) Do you have labeled training data for the four event types, or will annotation be part of this scope? 2) Is batch processing sufficient for now, or do you need real-time streaming support from day one? Looking forward to potentially working together. Thanks, Kamran

@devendrathakur12

i’ve done very similar recently built an audio event classifier with wav chunks, pytorch models, and react dashboards with timeline playback What sample rate and annotation format do you have for training slow snore vs normal flow? Do you need real time streaming or batch processing is enough for 8+ hour files? I suggest using windowed spectrogram + CNN/CRNN with class weighting to improve slow snore recall and reduce false negatives. I suggest precomputing embeddings and indexing timestamps so UI playback is instant without reprocessing audio. I will first build the preprocessing pipeline and label alignment, then train and validate the model with confusion metrics. Next I will expose a python API for per second labels and WOB scores. Finally I will build the react dashboard with timeline, gauges, and audio jump playback. Best, Dev S.

@techplusintl

Hi, I can develop a system to analyze audio recordings and provide insights through a web dashboard. This will help you easily identify acoustic events and visualize breathing metrics. I will use Python libraries like librosa and TensorFlow for audio processing and model training. The front end will be built with React to create an intuitive dashboard with gauges and audio playback features. I will ensure the slow snore detection is accurate and provide a confusion matrix report. Could you clarify if you have any specific preferences for the audio file format or the hosting environment for the web dashboard? Let's chat more about your project details! Thanks!

@Feriver

Hello, I understand you need an end-to-end AI system to analyze long-duration audio and surface clinically meaningful snore patterns with an interactive dashboard. I have strong experience in audio DSP, ML pipelines, and full-stack visualization, enabling accurate classification and real-time insight delivery. My approach uses feature-rich audio extraction (MFCCs, spectral flux, zero-crossing) with a hybrid CNN/RNN model to classify frame-level events. For slow snore detection, I will apply temporal smoothing, class rebalancing, and targeted validation with confusion matrix tuning to improve sensitivity. Work-of-breathing metrics will be derived as continuous indices from amplitude and spectral dynamics. The backend will expose a clean API for per-second labels and metrics, while the frontend will deliver a professional dashboard with gauges, dynamic sphere visualization, timeline navigation, and instant audio playback. Deliverables include model, reproducible pipeline, UI, and clear documentation. Thanks, Asif.

@artakharutyun7

Hello, This project fits well with my experience building systems that combine "audio signal processing, machine learning, and interactive web dashboards". For the detection pipeline, I would process the overnight WAV recordings using Python with libraries such as "librosa, NumPy, and PyTorch". The audio would be segmented into frames, converted to spectral features (Mel spectrograms, MFCCs, energy bands), and passed through a lightweight CNN/temporal model to classify the four acoustic states: heavy snoring, breathing interruption, normal flow, and slow snore. Special attention will be given to "slow-snore detection" by using longer temporal windows and feature smoothing to capture subtle low-energy patterns. Model validation will include a "confusion matrix and evaluation metrics" to ensure reliability. You will receive the trained model, inference pipeline, API, and a clean web dashboard with clear setup documentation. Best regards, Artak

@emilesd617

Hi, I am a full-stack AI developer with 8 years of rich experience with a background in audio analysis and data-driven web applications. I am familiar with Python, PyTorch, React, D3.js, audio processing. For this project, the most important part is detecting slow snore events accurately, because they are subtle and easy to miss in long recordings. I will build a reliable audio classification pipeline with validation for model quality, then connect it to a clean dashboard with timeline playback and live visual metrics so the results are easy to review. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@kishanbwts

I’ve worked on a sleep study project that processed long overnight audio to flag breathing events, so I understand how tricky slow snoring can be due to its subtle acoustic features. For detection, I would start with a frame-level spectrogram analysis combined with a lightweight neural network—something like a CNN-LSTM hybrid—to capture both short-term and temporal patterns. Balancing precision and recall on slow snore means carefully curating training data and implementing a confidence threshold tuned on validation data, plus providing a confusion matrix to track model performance. On the back end, I’d build a REST API in Python that processes WAV files with librosa and the model in PyTorch, outputting per-second labels and continuous work-of-breathing metrics. For the front end, React paired with D3 fits well for gauge visuals and the animated sphere, while in-place audio clips can be served as byte ranges from the original file, avoiding re-encoding delays. Two quick questions: Do you plan on running real-time streaming or only batch processing? Also, how consistent is the audio quality across recordings? This info helps decide model complexity and preprocessing. I’m ready to start building the pipeline and dashboard immediately, focusing on accurate slow snore flagging and smooth user interaction.

@Hubready

Hello!, I am a Florida-based senior software engineer with extensive experience in full-stack development. I’ve carefully read your project description for the AI Snore Pattern Analyzer Dashboard and I'm excited about the opportunity to contribute my skills in audio processing and data visualization. With over 15 years in the industry, I specialize in Python, Vue.js, and D3.js, ensuring that I can deliver a robust solution tailored to your needs. My approach emphasizes clear communication and structured milestones, which I believe are key to the project's success. Could you please clarify the following questions to help me better understand the project? 1. What specific audio features are you looking to analyze in the snore patterns? 2. Do you have any existing datasets or systems we should integrate with for the dashboard? I’m all about building systems that are not just functional but also intuitive and user-friendly. I’ve developed various applications that focus on data visualization, and I’m confident I can create a dashboard that meets your expectations. Let’s connect to discuss how we can turn this project into a reality! -James

@mykhaylob4

I've built audio processing pipelines that detect subtle breathing patterns from overnight recordings—snoring, interruptions, and those quieter "slow snores" that typical models miss. I'll turn your long audio files into a clean dashboard with real metrics and instant playback. ⏺️ For slow‑snore detection: I'd train a lightweight CNN on spectrograms with a custom loss that penalizes false negatives on low‑amplitude events, then post‑process with a temporal smoother. Librosa for features, PyTorch for the model. A validation report (confusion matrix per event type) proves accuracy before you trust it. ⏹️ Backend: Python inference pipeline that sweeps a WAV file, outputs per‑second labels plus work‑of‑breathing metrics (intensity and patient effort). REST API serves event timestamps and trimmed audio clips on demand. ⏺️ Dashboard: React with D3 gauges for intensity/effort, a central sphere that changes color/animation with cumulative findings, and a scrollable timeline to jump to any event and play a short clip instantly. I can share similar acoustic monitoring projects I've shipped. Let me know if you want to hop on a call to talk through the slow‑snore feature in more detail.

@Tanmoy236

Hello! As a seasoned full-stack engineer with over 9 years of experience in audio signal processing (librosa, PyTorch) and real-time dashboards (React, D3.js), I turn overnight recordings into actionable snore analytics. Here's how I can help: - Build an AI pipeline that classifies heavy snoring, breathing interruptions, normal flow, and slow snore with a confusion-matrix report - Calculate continuous intensity and patient effort (WOB) metrics per frame - Create a professional dashboard with circular gauges, animated central sphere, and scrollable timeline with audio trimming - Deliver REST API, trained model, and reproducible inference script For slow-snore detection: I'd use spectral centroid and zero-crossing rate features plus a lightweight CNN trained on labeled segments. To confirm: do you have any labeled training data, or should I design a self-supervised validation routine?

@AlexDeveloper911

Hey, I am ready when you are.✅ I’ve worked on something very similar. What really matters here is the complexity of accurately detecting slow snoring in audio recordings and presenting real-time insights. Most projects struggle with maintaining accuracy in acoustic event classification. I recently developed a system for real-time audio event detection using Python and TensorFlow, which involved processing continuous audio streams for specific patterns. While I haven't worked on snore detection specifically, I have experience with audio signal processing and classification tasks. Let's chat! -Oleksandr

@revival786

Greetings! I looked at your acoustic monitoring project. You need to detect four sleep events (heavy snoring, breathing interruptions, normal flow, slow snore) from overnight recordings, calculate two "work of breathing" metrics, and display everything in a web dashboard with gauges and a timeline. I provide full-stack AI and audio processing services. For slow snore detection, I will train a lightweight CNN or use a pre-trained model fine-tuned on labeled snore data. The challenge is distinguishing slow snore from normal breathing — I will focus on duration and spectral features (lower frequency content, longer exhalation phases). A confusion matrix will validate accuracy. Backend: Python with librosa for feature extraction and PyTorch for classification. The pipeline processes WAV files frame-by-frame, outputs per-second labels and two continuous metrics (intensity and effort). Frontend: React with D3 for gauges and a central sphere whose color and animation change based on cumulative findings. A scrollable timeline lets you jump to events and play trimmed clips without re-encoding. I have shipped similar audio classification and real-time dashboard projects before. I can share examples privately. Send me sample audio files and I will run an initial validation. Thanks, Revival

@SkillCrafts

I have deep experience at the intersection of audio signal processing and ML, specifically building health-tech tools that convert raw waveforms into actionable data. Having developed sound-event detection systems that isolate bio-signals from complex ambient noise, I understand the nuances of filtering interference to accurately map snoring events. Your project requires a robust pipeline that handles the temporal nature of sleep data while ensuring a high-performance frontend. My background in spectral analysis and full-stack architecture ensures I can build a dashboard that is both technically precise and intuitively designed for your users. I’ll start with a preprocessing stage using Librosa to extract Mel-spectrograms, which are vital for identifying the acoustic signatures of snoring. I propose a FastAPI backend to manage a lightweight CNN model optimized for pattern recognition, utilizing Redis for efficient task queuing during heavy processing. For the dashboard, I will implement a responsive React interface with D3.js to provide interactive visualizations of sleep architecture. This allows users to drill down into specific timestamps to review the intensity and duration of snore patterns across their sleep cycle. Will the analyzer process long-form recorded files or is real-time stream processing required? Do you have specific health metrics you want the dashboard to prioritize? I’m available for a quick chat to discuss the technical stack or review any existing datasets. Let’s connect to refine these details and build a solution that provides clear, meaningful feedback to your users.

@arslanshahid386

Hi there, I'm excited about the AI Snore Pattern Analyzer Dashboard project! You’re looking to analyze overnight audio recordings for specific snoring patterns and present that data in an engaging dashboard. With 4+ years of experience in full-stack development and audio processing, I can help you build a reliable AI pipeline to detect heavy snoring, breathing interruptions, and the subtle “slow snore” you mentioned. To tackle the slow snore detection challenge, I would leverage Python libraries like librosa for audio signal processing and potentially integrate a machine learning model using PyTorch or TensorFlow to enhance accuracy. For the front end, I’d create a user-friendly interface using React, ensuring that the gauges and timeline are both functional and visually appealing. Could you share more about the specific metrics you want to visualize in the dashboard? Best regards, Arslan Shahid

@AlexVydrin

Hello, this is exactly the kind of cross-disciplinary project I enjoy working on. I have experience building AI pipelines for time-series and audio analysis combined with data-rich dashboards. I would approach the detection using a hybrid DSP plus deep learning model, extracting features like MFCCs, spectral flux, and energy patterns, then training a classifier in PyTorch with special attention to slow snore differentiation using temporal context windows and class balancing. The backend would expose per-second labels and breathing effort metrics via an API, while the frontend in React would visualize gauges, animated states, and an interactive timeline with instant audio playback. I focus on accuracy, clarity, and a polished user experience, and I can deliver a clean, well-documented system ready for real use.

@krisk63

Hi I’m Kris, based in McKinney, Texas. I’ve reviewed your requirements and I understand you’re building an end-to-end system that processes long-form audio recordings (8+ hours), classifies acoustic events (snoring types, breathing interruptions, normal flow), and surfaces that data through a real-time or batch-driven dashboard. The key challenge here is achieving reliable classification—especially for subtle “slow snore” patterns—while keeping the pipeline efficient and the UI responsive for large audio files. Additionally, a few questions: Q1: Do you already have labeled data for the four event types, especially slow snoring, or will part of this require dataset creation? Q2: Should inference run locally on your machine, or are you open to a lightweight server/cloud setup? Q3: Do you need real-time streaming support, or is batch processing sufficient for now? Q4: Any preference for model interpretability (e.g., visualizing why a segment was classified a certain way)? If you share more about your dataset and constraints, I can outline a concrete implementation plan (model → validation → API → UI) and highlight trade-offs for accuracy vs performance. Regards, Kris

@shawanay

Greetings, I'm a senior AI developer with 7+ years of experience and here is my approach for your project: 1. Set up pipeline to process overnight audio recordings and classify events: heavy snoring, breathing interruptions, normal flow, and slow snore 2. Develop per-second metrics calculation for intensity and patient effort, with a model-validation routine for accuracy on subtle events 3. Build REST API or local service delivering inference results to the front-end dashboard 4. Create a professional monitoring web interface using React with circular gauges, animated central sphere, and scrollable timeline with in-place audio playback Provide documentation and reproducible scripts for model inference and dashboard setup I specialize in data-rich dashboards integrated with AI/ML pipelines, delivering real-time insights with clean, interactive front-end visualizations. By now, you likely have a strong sense of my experience. Let’s connect and turn your overnight audio recordings into actionable, visual insights!