Home

Ella: Sight Singing & Reading

Voice Pitch Analyzer & Trainer

Several designers, developers, music-education content creators, researchers, and music experts at Hirsh Group Software developed this project.

Hirsh Group Software (HGS) wanted to grow an MVP of Educational App into the leading sight-singing experience on the App Store. The focus of this project was to employ iOS design and development best practices to envelop the cutting-edge sound-analysis technology in a best-in-class app.

The target audience of this product is college level musicians taking classes in sight-singing and students going through the AP Music exams. The challenge was finding a way to convey sight-singing feedback with the necessary granularity for students and educators to measure performance through key metrics. The ultimate goal of the product is to respond to a student’s sight-singing with knowledgable feedback indistinguishable from a music tutor.

Being good at conveying automated feedback based on audio analysis had become increasingly important due to the rise of distance education. Reports state that music education has suffered much due to the contact restriction guidelines that followed the spread of COVID-19. As in-person classes are the preferred setting for sight-singing training, Ella was presented with an opportunity for supplanting the modes in which educators and students conduct their classes.

Audio Capture

The first step into conveying meaningful feedback that enriches in-class experiences is to create a mode of audio capture that contains most of the affordances of a music classroom: music notation, tuner, and metronome.

The idea was to decrease the student’s cognitive load by providing exactly what they need at that time and nothing more. Furthermore, additional interface features are gradually disclosed through unobtrusive tooltips.

Analysis

Secondly, the app has to provide specific feedback that helps both students and teachers to interpret the performance in meaningful ways. This decreases the cognitive load by displaying useful information and ways to reproduce and narrow specific segments of the performance.

To allow best-in-class navigation on all devices and content, we employed direct manipulation of the music staff elements. By selecting a note, the user is exposed to statistics about the fitness of their performance in intonation and interval, critical information for evaluating performance.

Music Staff

Several measures were taken in designing the music staff given the little space in iPhone screens, the majority audience. Sheet music looks and acts differently on iPhone and iPad to accommodate device size. The note spacing is compliant with the music notation standards while also having adaptions for minimum touch target size.

The music staff is the primary interface of manipulation of the recording and analysis. It allows both for reviewing the sequence and for narrowing down on specific notes. Its rendering is backed by musical fonts (SMuFL), thus being easily extensible and industry compliant.

Learning Path

Task selection is one of the fundamental problems of education technology. The solution chosen was to allow for fixed sequential progression of an expert-curated curriculum. This solution reduces the cognitive load of choosing where to focus your practice next while providing situational awareness of how far did the student advance.

Collections

Exercises were grouped by theme to help students can practice a particular facet of their performance. This interface acts both as a way to communicate proficiency and as a means for navigation.