SkyNote

Sound quality, intonation, rhythm, gestures and expression feedback in one App

SkyNote App

SkyNote App

What is it?

It is an intelligent music learning app based on sound and motion real-time analysis, and artificial intelligence technology.

Why use it?

It empowers students with tools to detect and correct errors, build awareness of their technique, and allow them to practise more efficiently alone or in a class setting.

Who is it for?

It is designed with a wide range of student levels in mind, from beginners to professional players.

Artificial Intelligence

Sound quality, posture, intonation, pitch, rhythm and expression in music performance

Sound quality

In instruments such as the violin, clarinet and trumpet, good sound production is one of the first skills to acquire. SkyNote allows you to improve it using Artificial Intelligence and Audio Analysis

Pitch/timing

In music performance playing the correct note (i.e. pitch) at the correct time (i.e. rhythm) is of paramount importance. SkyNote provides pitch and timing accuracy visual feedback to music students.

Posture

Mastering an instrument technique is of paramount importance. SkyNote provides students with real-time feedback on their gestures using Artificial Intelligence and motion capture techniques.

Bowing technique

Using Artificial Intelligence and audio analysis, SkyNote provides real-time feedback about the accuracy of bow stroke techniques such as Detaché, Martelé, Spiccato, Ricochet, Sautillé, Staccato and Bariolage, among others.

Research

Selected Papers

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A multimodal corpus for technology-enhanced learning of violin playing
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Enhancing Music Learning with Smart Technologies
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Real-Time Sound and Motion Feedback for Violin Bow Technique Learning: A Controlled, Randomized Trial
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Automatic Assessment of Tone Quality in Violin Music Performance
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Bowing Gestures Classification in Violin Performance: A Machine Learning Approach
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Technology Enhanced Learning of Expressive Music Performance
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Bowing Modeling for Violin Students Assistance
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Applying Deep Learning Techniques to Estimate Patterns of Musical Gesture

About Us

We are a team of researchers and developers experts in music and machine learning

Dr Rafael Ramirez

PhD in Computer Science.
Head of the Music and Machine Learning Lab (UPF))

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Dr Sergio Giraldo

PhD Computer Science, BA Music
Machine Learning and Expressivity models

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Dr David Dalmazzo

PhD Computer Science, BA Music
Machine Learning and Gestural models


Linkedin

Dr George Waddell

PhD Music Performance Research Associate Royal College of Music

Linkedin

Contact

Our Address
Carrer de Tànger, 122
CP 08018
Music Technology Group
Barcelona, Spain
: +34 935421365
: +34 935422517
: rafael.ramirez@upf.edu