AI Music Snippet Recommender Prototype

@Srashtasoft

Hi there, I have read your project requirement. You need an Android-based music app prototype with waveform segment selection, AI-powered similarity matching, a REST API layer, and a proof-of-concept ML service using audio feature extraction for fast recommendations. We can build this end-to-end using Kotlin/Flutter for the app, a lightweight REST API, and a Python-based ML service (librosa + cosine similarity) with precomputed embeddings for fast response. The solution will include a clean material UI, waveform selector, real-time results carousel, and optimized performance to meet your 3-second response target. A few questions to align the development: ================================= Do you prefer Kotlin (native) or Flutter for faster iteration? What is the expected size of your initial audio dataset? Should the ML inference remain server-side or partially on-device in future? Do you have sample audio files and metadata ready for testing? Best Regards, Srashtasoft Team

@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

@letswork92

I understand your challenge of creating an intuitive music app that leverages AI for real-time recommendations. The need to seamlessly extract audio features and deliver similar tracks quickly is crucial for user engagement. With over 12 years of experience, I specialize in building robust mobile applications using Kotlin or Flutter, ensuring a clean, responsive UI. I can develop the clickable prototype you envision, integrating a lightweight REST API that connects to an efficient AI micro-service. Leveraging libraries like librosa for audio processing and utilizing Firebase for metadata storage will ensure we meet your requirements swiftly. I also have experience with TensorFlow Lite, which can enhance our machine learning capabilities on-device for faster performance. My goal is to create a maintainable codebase that's easy to extend as your project evolves. Could you share more about any specific audio genres or datasets you'd like the AI service to focus on?

@abbas021

Hello I am a mobile and AI developer experienced in Android apps, audio processing, and lightweight ML pipelines. I can build your concept as a clean, modular prototype combining Android UI, a Python AI service, and a simple vector-based recommendation system. My approach: Android (Kotlin/Flutter): Waveform scrubber, segment selection (6–30s), “Compare” button, and a Material-style results carousel with album art and similarity scores. Dark, minimal UI using ExoPlayer and a waveform seek bar. Backend API: FastAPI endpoint that accepts timestamps/audio references and returns ranked track IDs + confidence scores. Built to be fast and extensible. AI Service: Python (librosa) for feature extraction (MFCC, tempo, chroma, spectral features). Precomputed embeddings stored for fast comparison using cosine similarity (upgradeable to FAISS later). Data Layer: SQLite or Firebase storing track metadata, embeddings, and artwork. Performance plan: Precomputed embeddings + cached top results to ensure <3s response on mid-range Android devices. Deliverables: • Android prototype (ready-to-run) • REST API service (Docker-ready or local run) • AI feature extraction script/notebook • Database schema + sample data • Full setup documentation I focus on clean architecture so you can later extend this into on-device ML (TensorFlow Lite) or a scalable cloud system without rewrites. Best regards, Abbas Ali

@haseebahmad222

Hi, I can easily DO your work IN 24 HOURS, DM me now to get started, PRICE NEGOTIABLE 100% Work satisfaction is provided.

@lestery3

Hi There , Good morning! I am professional mobile programmer with skills including Android App Development, JavaScript, Machine Learning (ML), Kotlin, REST API, Mobile App Development, Android and Java. Please contact me to discuss more about this project. For more details Chat with us

@patricko098

I recently completed an Android app project that streamlined audio analysis and improved recommendation speed by 40 percent. I’m new to Freelancer but have contributed to large scale projects at companies like Google and Amazon, focusing on multimedia and machine learning pipelines. My experience includes integrating Python DSP libraries and building clean Kotlin UIs. Your project clearly demands a simple, responsive interface with efficient audio feature extraction and scalable backend support. A user-friendly waveform selector paired with a fast, lightweight API aligns well with my approach. I understand the critical need for seamless integration of AI-driven similarity scoring and caching to meet your performance targets. I work by prioritizing clean architecture and straightforward solutions that are easy to maintain and extend. Building a robust foundation without needless complexity allows for reliable long term performance and smoother iteration cycles. I’m ready to start delivering results that meet your criteria and help bring your vision to life. If this aligns with your project, feel free to reach out to discuss scope and pricing. Regards Patrick

@indreshpnk

Hello, I understand you need an AI Music Snippet Recommender Prototype for Android where users can select a 6–30 second audio segment and instantly receive AI-based similar track recommendations. The goal is a fast, minimal, and functional proof-of-concept with waveform selection, compare action, and ranked results. Here’s what I can provide: • Android prototype (Kotlin/Flutter) with waveform scrubber, segment selector, Compare button, and results carousel UI • REST API stub (Python/FastAPI) that accepts clip/timestamp input and returns ranked track IDs with similarity scores • AI proof-of-concept using librosa to extract MFCC, tempo, key, and mood-style embeddings with cosine similarity search • Simple SQLite/Firebase setup to store metadata and precomputed feature vectors for fast retrieval I bring over 4+ years of experience in mobile and backend development, including Android applications, REST APIs, and ML-based media processing systems. I focus on building clean, scalable, and maintainable prototypes that can later evolve into production-ready systems. Just to clarify a few things: • Do you want the first version optimized more for speed or recommendation accuracy? • Will you provide initial audio dataset or should I use sample/open datasets? • Should the system be designed for future real-time on-device inference (TFLite) or cloud-first scaling? Let’s connect in chat to refine the approach and move quickly. Best regards Indresh Kushwaha

@mohanm115

Hello, I read your requirement for the AI Music Snippet Recommender Prototype and I find it very interesting. This aligns well with my background in AI/ML and full-stack development. I can build a functional Android prototype using Kotlin or Flutter with a waveform selector, compare feature, and results carousel. I will also create a lightweight backend API to process audio snippets and return similarity-based recommendations. For the AI part, I can use Python with libraries like librosa to extract features such as tempo, pitch, and timbre, and compare them using similarity algorithms. I can also integrate precomputed embeddings for faster performance. I have experience working on machine learning projects and building scalable applications, and I will ensure clean UI, fast response time, and proper documentation for future improvements. I am ready to start immediately and can deliver a working prototype within your timeline. Looking forward to working with you. Thanks, Mohan

@byronw7

As a South African, I know what it is to work hard and succeed at anything you do and complete tasks at 100%. I’ve handled similar projects requiring a clean, professional, user-friendly, and seamless design with integrated, automated backend services. Your need for a clickable Android prototype with waveform selection, a lightweight REST API, and a proof-of-concept AI service matches my experience perfectly. I specialize in Kotlin and Python development, audio feature extraction using librosa, and SQLite/Firebase integrations. While I am new to freelancer, I have tons of experience and have done other projects off site. I would love to chat more about your project! Regards, Byron Walbrugh

@KSHITIJ8713

Hi, I can help develop your AI-powered Android music recommendation prototype. I have experience with frontend/mobile development and can assist in building: * Android UI with waveform selector and results carousel * Clean Material-style dark theme interface * REST API integration * Audio upload and comparison flow * SQLite/Firebase integration * Basic AI/audio feature extraction workflow using Python tools like librosa I understand the overall concept of segment-based music similarity and can help create a maintainable proof-of-concept prototype with clean code and responsive performance. I am available at ₹300/hour and would be happy to discuss the project further.