Music Source Separation
We develop your AI solution. With expertise in advanced spectral analysis, audio processing algorithms, and modern AI techniques, our team tailors custom designs blending traditional signal processing with AI.
The task of extracting individual audio components from a mixed track, known as Music Source Separation, presents a surprisingly complex challenge given the ease with which audio signals from different sources can be combined into a single track.
This area of study has attracted a substantial amount of research due to its wide range of applications. Source separation not only allows professional high-quality editing of a recording that has been captured with consumer-level equipment, but additionally yields use cases that go beyond audio editing.
Musical education is just one example of domains where source separation can fruitfully be deployed to enhance learning experiences.
In the light of the current state of technology, establishing a music source separation system requires careful engineering and optimization to yield optimal results.
We anticipate substantial progress in this field in the near future, e.g. based on guiding techniques with diffusion models. Our expertise not only encompasses the selection of the most suitable models for current applications, but we also keep track of the newest developments in the field to be able to select and deploy the most useful cutting-edge techniques, allowing us to construct the most powerful music processing systems for any need.
Our Toolset
Let’s Create Together
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How do we build your product?
We can support in every phase of product development. To enhance productivity and accelerate time to market, we typically employ a proven, systematic approach to collaboratively build the product with you.
01
Concept and Ideation
Define the core idea, identify the problem we are solving, the target audience, and the essential differentiating features.
02
Feasibility Study
Assess the technical feasibility and market viability of the product through preliminary research and analysis.
03
Planning and Design
Outline the scope of a Minimum Viable Product (MVP) focusing on essential features only, and plan the project timeline, budget, and resources. Design initial wireframes or mockups.
04
Development of an MVP
Develop the MVP using rapid development cycles with continuous integration and testing, focusing on creating a functional prototype.
05
Testing and Iteration
Test the MVP internally and with users to gather feedback, and iteratively refine the product based on this feedback.
06
Launch
Officially launch the MVP to a broader audience, including marketing and user support preparations.
07
Evaluation and Scaling
Analyze the performance of the MVP against objectives, and scale the product by adding features and expanding the market reach if the MVP is successful.
Why Choose Us
Discover the reasons that set us apart.
Proficiency
With extensive expertise in audio processing, machine learning, and software development, we are the ideal partner for implementing audio and AI projects.
Approach
We use modern development tools and project management procedures to maximize productivity while retaining the highest quality outcomes for your product.
Team
We are tech enthusiasts, innovators and we live music. We’re driven to revolutionize your product by harnessing the latest in AI advancements.
12+
Years of Deep Tech experience
100%
Client satisfaction
1400+
GitHub Stars
3 Countries
Our team is based in Austria, India and the USA
Why use AI?
Enhanced Audio Quality
AI algorithms can automatically improve audio clarity, reduce noise, and optimize sound quality. This is particularly useful in environments with variable acoustic conditions, enabling consistent audio quality across different scenarios.
Personalization
AI algorithms can adapt audio content to the preferences of individual users. For instance, streaming services use AI to recommend music based on listening habits. In software interfaces, AI can adjust the audio dynamics to suit user-specific hearing profiles.
Cost Efficiency and Scalability
Automating various audio processing tasks with AI reduces the need for manual intervention, lowering costs and improving efficiency. AI systems can scale more easily than human-based systems, managing large-scale audio processing tasks without a proportional increase in resource investment.
Our Story
PhonicScore is the CultTech company from Vienna, Austria, the city of music, focusing on Music and Education. Since 2012 we are developing mobile apps, software libraries, plugins and AI solutions.
Let’s Create Together
Connect with us to explore how we can make your vision a reality. Join us in shaping the future.