Native real-time audio analysis module (iOS & Android)

@AwaisChaudhry

Hi Gabriel, I will deliver a standalone native audio analysis module for iOS (Swift) and Android (Kotlin) that’s ready for React Native integration. It will capture microphone input with low latency, provide real-time RMS and peak levels, real-time FFT spectrum with 256 or 512 bins, and pitch estimation with smoothing. The API will be simple: startMic(), stopMic(), onLevel(), onSpectrum(), onPitch(). I will ensure thread safety, proper cleanup, and clear build instructions plus a small demo to validate mic, level, spectrum, and pitch. Full source ownership will be transferred. Approach: native cores for each platform (AVAudioEngine on iOS, AudioRecord on Android), a light DSP layer for FFT and pitch, and a tiny bridge to JavaScript. I’ll keep the interface stable and documented, with a focused demo screen to show all features. Milestones: mic+level, spectrum, pitch detection, with tests and cleanups in the final pass. Timeline: 3-4 weeks in total, with weekly check-ins and fast feedback on any API changes. Milestones and pricing: fixed price $1200, with three milestones: mic+level, spectrum, pitch. What’s the single most important constraint to confirm before starting: target iOS/Android versions and the React Native bridge approach? Best regards,

@irsolutions

With over 10 years of experience in web and mobile development, specializing in real-time audio processing, I understand the importance of building a robust native audio analysis module for iOS and Android. Your project requirements align perfectly with my expertise, and I am excited to offer my services to help you achieve your goals. I have a proven track record in real-time audio work, having successfully developed similar modules in the past. My approach to pitch detection involves using advanced algorithms to ensure accurate and stable readings. I can guarantee a timely delivery within the specified period of 20 days, with a fixed price estimate that is fair and split into milestones for transparency. I am confident that my skills in native app development, combined with my experience in real-time audio processing, make me the ideal candidate for this project. I look forward to collaborating with you and delivering a high-quality, fully functional native audio module that meets your requirements. Let's discuss further how we can make this project a success.

@corpmember29

I HAVE BUILT NATIVE REAL-TIME AUDIO ENGINES FOR MOBILE — READY TO DELIVER A HIGH-PERFORMANCE ANALYSIS MODULE FOR YOUR REACT NATIVE APP. Hi Gabriel, I have hands-on experience building low-latency audio engines for iOS and Android, including real-time FFT, custom DSP pipelines, and pitch-tracking modules for music apps, voice tools, and metering systems. I’m comfortable delivering clean, native Swift/Kotlin code with a React Native bridge and proper threading/resource management. 1. Previous Real-Time Audio Work Built a real-time tuner (iOS/Android) using native audio units + DSP (FFT + autocorrelation). Developed audio metering modules with RMS/peak detection and smoothing filters. Created custom spectrum analyzers for music production apps (512–2048 FFT bins). Delivered React Native bridges for streaming, audio callbacks, and DSP processing. 2. Pitch Detection Approach I recommend a hybrid YIN / autocorrelation-based algorithm: Very stable for low-latency mobile input Accurate for speech + musical tones Smoothable with median filters / exponential averaging This ensures a clean, stable fundamental frequency and cents deviation. 3. Expected Timeline Total: 2.5 – 3.5 weeks Week 1: Mic + low-latency capture + RMS/peak module Week 2: FFT + magnitude spectrum with configurable bins Week 3: Pitch detection + smoothing + RN bridge + demo screen

@leadconcept

Greetings, I understand you need a standalone native audio module for iOS (Swift) and Android (Kotlin) that captures real-time microphone input, measures RMS and peak levels, performs FFT for spectrum analysis, and detects pitch all with low latency, stable buffers, and a simple API consumable from React Native. UI and product design are out of scope. Before we proceed, could you clarify, 1, Are there preferred FFT libraries or frameworks you want used, or should we select the most performant options? 2, Should pitch detection prioritize speed or accuracy, considering smoothing for stable output? 3, Do you want the demo/test screen implemented natively or via a minimal React Native wrapper? Our team includes developers with experience building real-time audio processing modules for mobile, including FFT analysis, RMS/peak measurement, and pitch detection using autocorrelation or YIN algorithms. We focus on thread-safe, low-latency implementations with clean APIs for React Native integration. Let us connect to confirm milestone structure, timeline, and pricing. The current bid amount is a placeholder to start the conversation. Regards Yasir LEADconcept PS: I can share examples of previous real-time audio modules and demos if you would like to review relevant work.

@Julian29

Hi Gabriel, I reviewed your requirements carefully and clearly understand the need for a standalone, low-latency native audio analysis module for iOS (Swift) and Android (Kotlin), designed to be cleanly consumed by React Native with no UI scope. I have 10+ years of experience working with real-time audio processing on mobile, including microphone capture, buffer management, FFT analysis, and pitch detection for music and voice-driven applications. I’ve built native audio engines using AVAudioEngine / AudioRecord, with real-time callbacks, thread-safe data delivery, and proper lifecycle cleanup. Pitch detection approach: I would use a YIN or autocorrelation-based algorithm (with smoothing and median filtering) for stable fundamental frequency estimation, along with cents deviation calculation relative to equal temperament. This balances accuracy, performance, and real-time stability on mobile devices. Expected timeline: • Mic + level: 3–4 days • FFT / spectrum: 3–4 days • Pitch detection: 4–5 days Total: ~10–12 working days Fixed price (milestones): • Mic + RMS/Peak level • Real-time FFT spectrum • Real-time pitch detection (Exact quote can be finalized after brief confirmation) All code will be fully native, well-documented, work-for-hire, and delivered with demo/test screens and clear build instructions. I eagerly await your response. Thanks

@diektech

Hi Gabriel, this is Elias from Miami. I’ve reviewed your project description, and I understand that the goal is to build a standalone, native audio module for iOS (Swift) and Android (Kotlin) that captures real-time microphone input, calculates RMS/peak levels, performs FFT analysis, and estimates pitch—all with low latency and a clean JavaScript API for later React Native integration. With over 10 years of experience in audio DSP and mobile development, I’m very interested because I’ve delivered real-time audio modules and low-latency DSP pipelines for iOS and Android. A few questions to clarify before starting: Q1: Do you prefer FFT bin counts fixed at 256/512, or should the module allow configurable sizes for different visualization resolutions? Q2: For pitch detection, do you want an autocorrelation-based approach, YIN, or are you open to other efficient algorithms optimized for mobile? Q3: Should RMS/peak and FFT updates be pushed at a fixed refresh rate, or adaptively based on processing load? Q4: Any constraints on CPU/battery usage for continuous audio processing on mobile? I’d love to outline a milestone plan: Microphone input + level detection FFT / spectrum analysis Pitch detection Looking forward to delivering a fast, stable, and fully documented module ready for React Native. Regards, Elias

@efanntyo

Hi Gabriel, I’m a mobile engineer with hands-on experience building real-time audio processing modules on both iOS and Android, focused on low-latency capture, stable buffering, and live analysis (levels, FFT, and pitch). I’ve implemented native audio layers intended for consumption by cross-platform apps, including React Native, with clean JS-facing APIs and careful lifecycle management. For pitch detection, I’d use a YIN or autocorrelation-based approach (with post-processing and smoothing) for stable real-time fundamental frequency estimation and cents deviation, balancing accuracy and performance on mobile CPUs. I can deliver full native source code for iOS (Swift) and Android (Kotlin), clear build instructions, and a simple demo/test screen validating all features. All work can be provided as work-for-hire with full IP transfer. Best regards

@pinkeegupta

Hi I have read your requirements and I am sure I will be able to help you. Please message me so that we will have detail technical discussion. I have 9+ years of combined experience in Mobile Application development, Website development, Desktop application development, 3rd party Artificial Intelligence api, AR/ VR, Chatbot, Blockchain- Cryptocurrency, CRM & ERP, Game Development and any other Software development. I am having expertise in Native on Android Java, kotlin and IOS Swift, and For Hybrid Cross platform on Flutter Dart and for web and backend on react js and node js, Python Django, Power BI, Scikit-learn, Data Science AI ML, PyTorch, TensorFlow, SparkML , java spring boot and php CodeIgniter mvc. Please consider me and initiate a chat for further detailed discussion. Regards, Anju

@Mahihassi

As an experienced Software Engineer with a Master's degree in Embedded Systems, I am excited about your Native real-time audio analysis module project. I have a strong background in firmware development and extensive experience in Digital Signal Processing and Mobile App Development, making me confident in my ability to deliver a performant and reliable solution for your iOS and Android platforms. I have consistently delivered efficient and scalable software solutions throughout my projects. To address your specific needs, I will leverage my knowledge of C/C++ programming to develop highly optimized code for low-latency audio capture, stable buffer delivery, audio level measurement (RMS+peak), FFT/Spectrum analysis, and real-time pitch detection. For the latter, I would suggest using the autocorrelation algorithm combined with peak picking since it is an established approach offering good results even in noisy environments.

@rbtech1984

Hello There!!! ⚜⭐⭐⭐⭐⚜(( Native low latency audio analysis module for iOS and Android ))⚜⭐⭐⭐⭐⚜ This is a focused and well scoped audio task, which is refreshing. You are not asking for UI or product decisions, but a reliable native audio layer that delivers clean real time data to a React Native bridge. The core objective is a standalone Swift and Kotlin module that captures microphone input with low latency, performs live analysis, and exposes predictable callbacks to JavaScript. I have worked on mobile audio pipelines involving real time capture, buffering, FFT processing, and pitch estimation where thread safety and stability were non negotiable. My approach would use platform native audio engines tuned for low latency, with FFT based spectrum analysis and a proven pitch detection method such as YIN or autocorrelation with smoothing to ensure stable readings. The API would stay minimal, predictable, and safe to consume from React Native. Key features I would focus on: * Low latency microphone capture with stable buffer delivery * Real time RMS, peak, and FFT spectrum output * Smooth and accurate pitch detection with clean callbacks If you would like, we can align quickly on the pitch detection method, milestones, and timeline before starting implementation. Warm Regards, Farhin B.

@Riasatali78

I can build a native Kotlin + Swift audio module with low-latency mic input, real-time RMS/peak levels, FFT spectrum, and stable pitch detection (YIN/autocorrelation), exposing a clean JS API for React Native. Delivery includes full source, build instructions, and a demo proving all features. Quick questions: preferred FFT size/update rate, voice vs instruments focus, and target devices/OS for optimization?

@ChiragDeol

Hello Gabriel, Thank you for sharing such a clear and focused brief—your approach to modular audio analysis for React Native is both practical and well-structured. Given your requirements, I recommend leveraging Apple’s AVAudioEngine (Swift) and Android’s AudioRecord/AudioTrack APIs (Kotlin) to ensure real-time, low-latency performance with stable buffer management. For cross-platform consistency, structuring the native modules with a unified callback pattern will make your future JS integration seamless and maintainable. I’ve delivered several real-time audio solutions, including speech signal analysis tools and live streaming audio processors for mobile apps. My work has involved implementing custom FFT routines, real-time pitch tracking, and efficient native-to-React Native bridges while ensuring thread safety and optimized resource handling. For pitch detection, I would propose using an auto-correlation method or YIN algorithm—both are proven in mobile environments for their accuracy and efficiency without excessive CPU load. This approach delivers stable frequency estimates suitable for musical applications or vocal analysis. Here’s how I would structure the project: **Milestone 1: Microphone Input & Level Meter** - Implement real-time mic capture with low-latency settings on both platforms. - Provide RMS/peak level callbacks. - Timeline: 1 week - Fixed Price: $1,000 **Milestone 2: Real-Time FFT/Spectrum** - Integrate efficient FFT libraries (e.g., vDSP/FFTW for iOS, native libraries for Android). - Expose magnitude arrays at configurable bin sizes. - Timeline: 1 week - Fixed Price: $800 **Milestone 3: Pitch Detection** - Develop robust pitch tracking using auto-correlation/YIN with smoothing. - Deliver readings in Hz plus cents deviation. - Timeline: 1 week - Fixed Price: $900 **Total Fixed Price:** $2,700 All deliverables will include full source code, integration instructions, a demo/test screen as you specified,

@creativeleads

For pitch detection, I’ll use a time-domain approach (YIN / autocorrelation-based) optimized for real-time performance, with post-smoothing to stabilize readings and avoid jitter. This gives reliable fundamental frequency estimation without excessive CPU cost.

@mughiraaa

Hi Gabriel, I have reviewed the details of your project. We have extensive experience in developing real-time audio solutions for mobile platforms, including custom audio processing modules for both iOS and Android, which are optimized for low latency and stability. Our approach involves using platform-specific APIs—Swift for iOS and Kotlin for Android—to capture microphone input efficiently. For pitch detection, we typically utilize the autocorrelation method, which offers accurate real-time fundamental frequency estimation with smoothing to ensure stable readings. This method has proven reliable in similar projects, providing precise and responsive results. Let's have a detailed discussion, as it will help me give you a complete plan, including a timeline and estimated budget. I will share my portfolio in the chat to demonstrate our previous work. Best regards, Mughiraa

@inciterzt

Hi Gabriel, I’d be glad to help you build a standalone native audio analysis module for iOS (Swift) and Android (Kotlin), designed cleanly for integration into a React Native application. Relevant experience: I’ve worked on real-time mobile audio processing systems involving low-latency microphone capture, RMS/peak metering, FFT-based spectrum analysis, and pitch detection for voice and music-focused apps. My experience includes AVAudioEngine/Audio Units on iOS and AudioRecord/Oboe on Android, with strong attention to buffer stability, threading, and resource cleanup. Pitch detection approach I would use a YIN or YIN-variant autocorrelation algorithm for fundamental frequency estimation, combined with temporal smoothing (EMA or median filtering) and cents deviation calculation. This approach is accurate, stable, and well-suited for real-time mobile use. Timeline • Mic input + RMS/Peak: 3–4 days • FFT / Spectrum: 3–4 days • Pitch detection + smoothing: 4–5 days Total: ~10–13 working days including testing and documentation. Clarifying questions • Target buffer size and latency expectations? • Preferred FFT bin count and update rate? • Primary use case: voice, music, or mixed input? • Minimum iOS/Android versions to support? All deliverables will be provided as work-for-hire with full ownership transfer, including source code, setup instructions, and demo/test screens. Best regards, Inciterz Tech

@amjad2

Hi Gabribro! Hi Gabriel, I’ve reviewed your project and specialize in building real-time audio processing modules for mobile platforms. I can create a standalone native audio module for both iOS and Android with features like low-latency microphone input and real-time pitch detection. I’d love to discuss the details further. Let’s set up a quick meeting! Best Regards, Amjad Iqbal

@maryam951

Hello Gabriel B., I am Maryam Abbas, a Mobile App Development expert with 4 years of experience in Android App Development, iOS Development, and React Native. I have carefully reviewed your project requirements for the native real-time audio analysis module. To address your needs, I will develop a standalone native audio analysis layer for iOS and Android, ensuring microphone input, audio level measurement, FFT/spectrum analysis, and pitch detection features. The module will have a clean API for easy integration with React Native. I have a proven track record in delivering successful projects and invite you to review my portfolio for more insights. Let's discuss your project further. Please find my portfolio links https://www.freelancer.pk/u/maryam951 and start a chat to explore the details. Best regards, Maryam Abbas

@shushmobiweb6

Hello With 8+ years of experience in mobile engineering and digital signal processing, I understand your key priority: a precise, low-latency native audio layer that is reliable, cleanly abstracted, and ready to be consumed by React Native without UI or product overhead. This module must be technically solid, thread-safe, and future-proof, not a prototype. → Stable real-time microphone capture with predictable buffering → Accurate, smooth RMS/peak, FFT, and pitch data suitable for live visualization → A minimal, well-documented native API that integrates cleanly with JavaScript Your scope aligns perfectly with how I approach native audio work—focusing on performance, deterministic behavior, and correct lifecycle handling on both iOS (AVAudioEngine) and Android (AudioRecord). Special attention is given to smoothing, frequency stability, and memory safety so the module performs consistently across devices. Since this will become a foundational layer for your React Native app, I prioritize clarity in the exposed API, strict resource cleanup, and ownership-ready source code that another team can extend with confidence. Want a dependable native audio core that “just works” and doesn’t need babysitting once integrated, I’m ready to take this forward with you. Thanks, Sushma S.

@ektatdsl

Hi Gabriel, I’m a senior mobile developer with hands-on experience building low-latency, real-time audio processing modules on both iOS and Android. I’ve worked on native audio engines for music, voice analysis, and real-time visualization use cases, including mic capture, FFT pipelines, and pitch detection optimized for mobile performance and stability. Relevant experience: On iOS, I’ve built audio pipelines using AVAudioEngine with custom tap buffers and Accelerate/vDSP for FFT and signal analysis. On Android, I’ve implemented low-latency mic capture using AudioRecord, optimized buffer sizing, and real-time DSP using Kotlin/NDK-friendly patterns. Several of these modules were later bridged into React Native apps via clean native APIs. Pitch detection approach: I’d use a proven real-time algorithm such as YIN or McLeod Pitch Method (MPM), depending on noise tolerance requirements. Both provide accurate fundamental frequency detection; I typically add temporal smoothing and confidence thresholds to stabilize readings and compute cents deviation reliably. I’m comfortable delivering clean, standalone native modules with proper threading, lifecycle handling, and clear JS-facing APIs, along with a simple demo screen to validate all features. Happy to discuss details and get started quickly. Regards, Ekta