Build Self-Hosted Audio Content Recognition (ACR) System for 10–20 Live TV Streams – Real-Time Music Detection + Dashboard

@irsolutions

With over a decade of experience in web and mobile development, specializing in real-time audio processing and live streaming technologies, I understand the challenges of building a self-hosted Audio Content Recognition (ACR) System for multiple live TV streams. The key requirements of dynamically adding/removing streams, continuous recording, and real-time audio extraction align perfectly with my expertise. I have successfully delivered projects in the audio fingerprinting domain, integrating open-source solutions like Dejavu and AcoustID. My experience with FFmpeg, live streaming, and building clean web dashboards ensures a seamless execution of your project. I am equipped to handle the complexities of unstable streams, parallel processing, and storage optimization, ensuring high accuracy and minimal latency in audio detection. For a quick start, I propose to integrate the JioTV repository, stream 2-3 channels, and demonstrate audio fingerprint detection on a basic dashboard. Once approved, I will scale up to 15-20 channels swiftly. I look forward to discussing your project further and delivering exceptional results for your real-time music detection system. "I have read and understood the full requirements. My approach involves leveraging FFmpeg, open-source audio fingerprinting libraries, and a modern web dashboard for seamless monitoring. Here are links to similar past projects for your reference." Let's collaborate to bring your vision to life.

@ZohaibRoy

⭐⭐⭐⭐⭐ Build Self-Hosted ACR System for Live TV Streams with Real-Time Detection ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for an experienced freelancer to build a real-time audio recognition system. You don’t need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in audio processing. I will create a robust system that records, analyzes, and detects audio from multiple live streams using a self-hosted fingerprinting engine. ➡️ Why Me? I can easily build your audio recognition system as I have 5 years of experience in audio processing, FFmpeg, and real-time streaming. My expertise includes live stream management, audio fingerprinting, and web dashboard development. I also have a strong grip on parallel processing and storage optimization to ensure high accuracy and performance. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ FFmpeg ✅ Audio Fingerprinting ✅ Live Streaming ✅ Real-Time Processing ✅ Web Dashboard Development ✅ Parallel Processing ✅ Audio Analysis ✅ Data Logging ✅ Storage Management ✅ M3U8 Streaming ✅ Python Programming ✅ API Integration Waiting for your response! Best Regards, Zohaib

@n2rtechnologies

Hi there, I reviewed your requirements and this looks like something we can handle well. Building a self-hosted ACR system that processes 10–20 live streams in parallel is exactly the kind of real-time audio processing work we've done before — the dashboard piece ties in nicely with our web dev background. I have a couple of questions about your infrastructure preferences and whether you're leaning toward specific audio fingerprinting libraries. Happy to jump on a quick call to discuss. I have delivered 1500+ web and mobile projects over 14+ years — happy to share relevant examples. Thanks, Hasan

@kamran2012

Hi there, I have read and understood the full requirements. I will build your self-hosted ACR system with FFmpeg-based multi-stream ingestion, real-time audio extraction, local fingerprint matching using Dejavu, and a responsive web dashboard showing detections with channel name, timestamp, and flagged clips. For the approach - I will use FFmpeg to pull M3U8 streams into segmented audio chunks, feed them into a Dejavu fingerprint database running locally, and handle parallel processing via a task queue (Celery + Redis) so 20 channels run without bottleneck. For unstable IPTV streams, I will implement auto-reconnect with exponential backoff and health monitoring per channel. One key consideration: mixing commentary with music degrades match confidence, so I will add a band-pass filter on the audio before fingerprinting to isolate the music frequency range and improve detection accuracy significantly. For the POC, I will integrate the JioTV repository, fetch working M3U8 links, stream and record 2-3 channels, run fingerprint detection, and display results on a basic dashboard - all within the first milestone. Questions: 1) Do you have a reference fingerprint database ready (the songs to detect), or should the system learn from segments you manually flag? Thanks and best regards, Kamran

@iglobalvn

Hello, I have read and understood the full requirements. I can build a fully self-hosted ACR monitoring system that records and analyzes multiple live streams in real time. My approach would use FFmpeg for pulling and segmenting M3U8 streams, with parallel workers extracting audio and sending segments to a local fingerprinting engine (Dejavu / similar Shazam-style library) backed by a PostgreSQL or Redis index. A queue-based pipeline (Python + asyncio/workers) will handle 10–20 streams efficiently while managing unstable IPTV sources. Detected matches will be logged with timestamps and exposed through a simple web dashboard (FastAPI + React) showing channel, detection time, segment clip, and playback preview. For the POC, I will integrate the public JioTV repository, fetch working stream links, run 2–3 parallel channels, perform fingerprint detection, and display results on a monitoring dashboard. The architecture will already support scaling to 15–20 channels with queue workers and segmented recording. Key focus areas: • Reliable stream capture + automatic reconnect • Low-latency fingerprint matching even with noise/commentary • Storage rotation for continuous recordings • Simple UI to add/remove stream URLs I can start the POC immediately and provide regular progress updates. If the POC is approved, scaling to the full system will be straightforward with the same architecture.

@amgadmourad18

Hi, I have read and understood the full requirements. I’ve worked on live stream processing and real-time media analysis systems, and this project fits well with my experience. My approach is to build a stable pipeline where FFmpeg continuously pulls and records the M3U8 streams, extracts audio segments in real time, and feeds them into a self-hosted fingerprinting engine (Dejavu / Chromaprint or similar) with a local database for Shazam-style matching. For the POC, I will: • Integrate the public JioTV repository and retrieve working stream links • Stream and record 2–3 channels simultaneously • Run real-time fingerprint detection on audio segments • Display detections with timestamps on a simple monitoring dashboard The system will be designed from the start to scale reliably to 15–20 streams, handle unstable IPTV sources, maintain low detection latency, and manage recordings efficiently. I’m comfortable with the $2,000 milestone structure and can start immediately. Happy to discuss the architecture and share relevant media/stream-processing work.

@hien223234

✅Hi, Client. I am a senior Python/C# developer✅ “I have read and understood the full requirements” I have successfully completed several projects similar like yours. I am interested in your project. I would like to work for you in the long term. Please send a message to discuss this project. I look forward to hearing from you. My main goal is to gain my client's satisfaction by completing a job with 100% accuracy I am a senior Python/C# developer with over 10 years of rich experience in C#/C/C++/QT/Java/Python/tesseract OCR/OpenCV/ML Programming, API integration/Database management. So, I can complete it within your timeline. Best regards! From Hien ...

@vinod00k

Hello I am full-stack developer specialising in database management and Python, I am fully equipped to handle your ambitious ACR project. My previous work in implementing audio fingerprinting - particularly with Dejavu and AcoustID - is a solid proof of my abilities. Alongside this, I have a strong command over frameworks like FFmpeg, which will be critical for ensuring efficient audio extraction from multiple streams as well as intelligent parallel processing for 20+ channels. It's worth mentioning that working with live streams can sometimes be unpredictable, but my hands-on experience in building robust systems has equipped me with the skills to navigate through such challenges effectively. Moreover, I understand the importance of real-time detection with minimal latency and I am confident that my technical prowess backed by deep knowledge of live streaming and real-time audio processing would prove invaluable in delivering a smooth solution. Please come over chat, thanks Vinod

@KarthikTechCon

Hello, I have read and understood the full requirements. I’m Karthik, a Senior Developer with 15+ years of experience in real-time streaming systems, media processing, and scalable backend architectures. I have strong experience with FFmpeg pipelines, live stream monitoring, and audio fingerprinting systems. Approach: • Use FFmpeg workers to pull and record M3U8/JioTV/YouTube streams. • Extract audio segments in real time and run self-hosted fingerprint matching (Dejavu/Chromaprint). • Implement parallel processing to handle 10–20 streams reliably. • Build a simple responsive dashboard to log channel, timestamp, detected audio segment, and flagged clip. • Add stream recovery and storage rotation for unstable IPTV feeds. I can quickly deliver the POC with 2–3 channels, detection pipeline, and dashboard, then scale to 20 streams after approval. Looking forward to discussing the project. Best regards, Karthik Senior Software Engineer 15+ Years Experience

@chiragardeshna

Hi there, I have read and understood the full requirements Approach: FFmpeg cluster for parallel M3U8 ingestion (JioTV repo integration) Dejavu self-hosted fingerprinting with Postgres database Python asyncio for 20-stream concurrent processing React dashboard with live detection feed + clip playback Smart stream reconnection + rolling 30s audio window Experience: Built 15-stream radio ad monitoring detecting commercials with 97% accuracy using AcoustID/Dejavu. Handled unstable IPTV streams with auto-reconnect. Timeline: POC (3 streams) in 10 days → Full 20-stream in 21 days Budget: $1900 Thanks Chirag

@dataspro

Nice to talk you , After reading in detail the requirements of your project and concluding that they match my areas of knowledge and skills, I would like to introduce myself. My name is Anthony Muñoz and I am the lead engineer for DS Pro IT agency. I have worked for over 10 years in Backend and software development and have successfully done multiple jobs. It will be a pleasure to work together to make your project a reality. Please feel free to contact me. I´m looking forward to working with you. I really appreciate your time and remain attentive to any request or question. Greetings

@shabahathabib

I have read and understood the full requirements. I can build a self-hosted real-time ACR system for 10–20 live TV streams. My approach: Use FFmpeg to pull and record M3U8 streams, continuously extract audio segments, and run local audio fingerprint matching with an open-source library like Dejavu. Detected songs will be logged with timestamps on a responsive web dashboard, with smart storage and parallel processing to handle unstable streams efficiently. For the POC, I’ll integrate the public JioTV repository, fetch working M3U8 links, stream 2–3 channels, detect audio locally, and display results in a minimal dashboard. Once approved, scaling to 15–20 channels will follow the same architecture with optimized concurrency and storage management. Best Regard, Shabahat Habib...

@vanhuy19892011

Hello, I read your project about building a self-hosted audio content recognition system that monitors 10–20 live TV streams, records them, detects songs in real time using local audio fingerprinting, and logs detections to a dashboard. “I have read and understood the full requirements.” I’ve worked with streaming pipelines using FFmpeg, Python audio processing, and real-time monitoring systems. For this system I would build a pipeline that pulls M3U8 streams via FFmpeg, extracts audio segments continuously, generates fingerprints using an open-source engine (such as Dejavu or Chromaprint/AcoustID), and matches them against a local database. Detections would be logged with timestamps and exposed through a lightweight web dashboard while background workers handle parallel processing across channels. A few quick questions before starting the POC: – Should the fingerprint database contain a predefined list of songs, or should the system also learn new tracks automatically? – How long should recorded archives be stored before cleanup/rotation? – Do you prefer Python workers (Celery/RQ) or a containerized microservice setup for stream processing? – For flagged clips, should the system automatically cut the detected segment from the recording? I can start with the POC (2–3 streams) quickly and design the architecture so scaling to 20 streams stays stable and easy to maintain. Best Regards,

@emirh31

Hello, I have read and understood the full requirements. I have built live-stream monitoring and audio recognition systems using FFmpeg and open-source audio fingerprinting (Dejavu/AcoustID). My approach: fetch working M3U8 streams, extract audio in real-time, run local fingerprint matching, and log detections on a clean, responsive web dashboard. I will handle unstable streams, parallel processing for multiple channels, and smart storage management for continuous recording. The POC will cover 2–3 channels with accurate timestamped detections before scaling to 15–20 channels. Timeline: 3 weeks Budget: $2,000 Looking forward to hearing from you. Thank you.

@stefang19

Hello, I have read and understood the full requirements. I have experience building real-time media processing systems using FFmpeg, Python, and streaming pipelines, and I can implement a fully self-hosted audio fingerprinting solution that monitors multiple live channels simultaneously. My approach: • Use FFmpeg workers to pull and record M3U8 streams (JioTV/YouTube) while extracting audio in parallel. • Process audio segments through a local fingerprint engine (e.g., Dejavu/Chromaprint-based system) with a self-hosted fingerprint database. • Implement parallel processing workers to handle detection for multiple channels with minimal latency. • Store detections (channel, timestamp, segment, clip reference) and display them in a lightweight web dashboard for monitoring and adding new streams. For the POC, I will integrate the JioTV repository, stream and record 2–3 channels simultaneously, run real-time fingerprint detection, and display the results on a basic dashboard. Once validated, the system can scale to 15–20 channels with queue-based processing and optimized storage rotation. I can provide regular updates during development and demonstrate the working system once the POC is ready. Thank you, Stefan Grugic

@Hemantt841

I have read and understood the full requirements, and I’m excited about building your self-hosted ACR system for live TV streams. You need a real-time music detection setup that handles 10–20 M3U8 streams, including JioTV integration, with a clean dashboard showing detections and timestamps. Your project requires dynamically managing multiple live streams, continuous recording, and local audio fingerprinting without relying on cloud APIs. I appreciate the challenge of handling unstable IPTV streams and ensuring accurate detection even with mixed audio, plus the need for a responsive web dashboard to monitor activity clearly. I previously developed a real-time audio recognition platform using FFmpeg for live stream processing and integrated an open-source fingerprinting library similar to Dejavu. This included parallel handling of multiple streams with efficient storage and a web interface displaying detection logs, which aligns directly with your POC goals and scaling plans. I can deliver the POC with 2–3 working streams, live detection, and a basic dashboard within two weeks. Let’s discuss the next steps to get started promptly.

@Vsion2

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@albertoprata

Hello, I have read and understood the full requirements and the phased approach starting with the POC before scaling to 15–20 channels. My approach would be to use FFmpeg to ingest and segment the live M3U8 streams, extracting audio in near real time and passing those segments to a self-hosted fingerprinting engine (such as Dejavu or a custom Chromaprint-based pipeline) with a locally stored fingerprint database. The system will run parallel workers to process multiple streams efficiently, while recordings and detections are logged with timestamps. I’ll build a lightweight web dashboard to display channel name, detection time, matched song segment, and a link to the recorded clip. The architecture will also include stream health monitoring, auto-reconnect for unstable IPTV feeds, and storage rotation to manage continuous recordings without filling the disk. I have experience working with FFmpeg pipelines, live stream processing, and audio analysis workflows, and I’m comfortable building scalable monitoring systems like the one described. I’m ready to start immediately with the POC phase (2–3 channels streaming + detection + dashboard) and then expand to the full 20-stream system once validated.

@viktorc2

I have read and understood the full requirements. I can build a self-hosted, real-time ACR system for 10–20 live TV streams using FFmpeg for stream capture and an open-source audio fingerprinting library (like Dejavu) for instant song detection. I’ll create a responsive web dashboard showing channels, timestamps, and flagged clips, and design the system for efficient parallel processing and storage optimization. I’ve previously built live-stream monitoring and audio recognition systems for content detection; happy to share references and start the POC immediately.