Automatic music transcription of polyphonic audio
Sector: Education • Location: Sweden
Source: EU Funding & Tenders Portal
DoReMIR Music Research has already launched several successful products for music analysis and composition and has a large user base of monophonic audio analysis worldwide. The project builds on and extends a product suite called ScoreCloud with the focus on easy creation and distribution of music notation. The ScoreCloud concept is technically built on mobile and desktop apps connected with a ful
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Participants
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Company | Obfuscated Data |
Status
Original status | ended |
Taiyo status | Obfuscated Data |
Taiyo last update | 00-00-0000 |
Available timestamps | 00-00-0000 |
Available timestamp type | Obfuscated Data |
Contact
Contact name | Obfuscated Data |
Phone | 0000000000 |
ObfuscatedData@email.com | |
Address | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Description
Description | DoReMIR Music Research has already launched several successful products for music analysis and composition and has a large user base of monophonic audio analysis worldwide. The project builds on and extends a product suite called ScoreCloud with the focus on easy creation and distribution of music notation. The ScoreCloud concept is technically built on mobile and desktop apps connected with a full cloud based back-end. The product enables users to notate music directly from performance: a Google Translate for Music! The project will develop a low-cost, cloud-based, polyphonic audio transcription solution based on an interdisciplinary approach (musicology, acoustics, audio engineering, cognitive science and computing) and a user-driven design (agile iterative solution development with end-user participation in the context of music teaching and music composition). In order to circumvent the limitations of current automated transcription methods, the project uses a novel approach to musical and music signal analysis, by modelling and using high-level musical knowledge (about stylistic conventions, music cognition, etc.) and machine learning techniques. In addition to finding better solutions to certain analysis problems, the resulting systems will also be able to communicate their results in musically meaningful, high-level terms. |
Original sub-sector | Obfuscated |
Original Currency | USD |
Original budget | 000000000000000 |
Procurement method | Obfuscated Data |
Budget | 000000000000000 |
Location
Region | Obfuscated |
Country | Obfuscated |
State | Obfuscated Data |
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Location | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Source
Source reliability | High |
Data quality score | 100% |
Source | Obfuscated Data |
URL | obfuscated_data,obfuscateddata.com |
More Details
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