Is there an app which converts music into written music?

Converting music into written notation, also known as music transcription, is the process of listening to a piece of music and translating it into musical notation such as sheet music or tablature. The goal is to capture all the melodic, harmonic, and rhythmic elements of the audio recording in a written format that can be read, interpreted, and performed by musicians.

Transcribing music requires carefully listening to the recording multiple times to identify all the notes, chords, tempos, time signatures, and other musical details. The transcriber must have strong music theory knowledge and a trained ear to pick out the different parts and accurately notate them. Music transcription results in a detailed musical score that recreates the audio performance in standard notation.

The main benefits of converting music into sheet music include easier learning and practice for musicians, deeper analysis of compositions, preservation of musical works, and simplified music sharing. Written music notation allows musicians to study, interpret, and perform pieces more easily compared to working solely from audio recordings. Overall, music transcription transforms recordings into playable, shareable, and archivable musical scores.

Background on Music Notation

Musical notation has evolved over many centuries as a way to visually represent the elements of music. The earliest forms of musical notation emerged as far back as the 9th century, when the Catholic Church began documenting Gregorian chants using symbols called neumes to indicate the melodic contour and direction of the chant. This early notation conveyed the basics of melody and rhythm but lacked specifics like exact pitches and note durations (A Short History of Music Notation).

Over the next several hundred years, notation continued to develop into a more precise system, eventually leading to the emergence of modern staff notation in the 17th century. This five-line staff and system of symbols allowed composers to precisely indicate pitch, rhythm, expression, and other musical elements on paper (The Evolution of Music Notation). Staff notation remains the predominant system used in Western classical music today.

While standard notation is the most widespread, there are other types of notation systems used for different instruments and genres. Tablature notation shows the physical points to play on a fretted instrument like guitar or lute. Percussive notation is used for unpitched percussion parts. Graphic notation uses symbols, shapes, and visual elements to convey music. Jazz lead sheets provide chord symbols and melodies but leave room for improvisation.

Regardless of the exact system, musical notation allows composers to communicate musical ideas with great specificity. Performers are then able to interpret these symbols and bring the music to life.

Music Transcription Technology

In recent years, advances in audio analysis and machine learning have enabled the development of technology that can automatically transcribe music into notation. This has emerged as an active area of research and commercial development.

Audio transcription systems analyze an audio recording of music and attempt to identify the notes, rhythms, and other musical elements. The raw audio signal is processed to extract musical features like pitch, timing, timbre, and loudness. Machine learning algorithms like neural networks are then trained on large datasets to recognize patterns and convert the audio into musical scores or notation [1].

However, accurately transcribing polyphonic music (with multiple notes played at once) remains an open challenge. While modern systems demonstrate high accuracy for monophonic music (single notes), performance on complex polyphonic music can be significantly lower. Estimates suggest leading systems may achieve 70-90% note accuracy on piano recordings, but results vary considerably based on genre and recording quality [2]. Overall, there is still ample room for improvement before matching expert human transcription.

Top Apps and Their Features

There are several top apps that can convert music into sheet music notation. Here are some of the best options:

NotateMe

NotateMe is one of the most full-featured music transcription apps available. Key features include:

Converts audio files like MP3 and WAV to sheet music
Has advanced music notation and editing capabilities
Extracts multiple instruments/voices into separate tracks

Includes tempo/time signature detection

NotateMe offers transcription capabilities for instruments like piano, guitar, drums and more. It can be used by musicians, composers, teachers and students. See full review: https://tabcreatorsoftwareonline.wordpress.com/2019/02/04/review-of-best-music-transcription-apps-in-2019/

Transcribe!

Transcribe! is designed to help transcribe recorded music. It provides features like:

Slows down audio without changing pitch
Filters to remove vocals or isolated instruments
Markers and loops for repetition

Partial/incomplete transcription export

Transcribe! makes it easier to hear and identify notes to transcribe into sheet music. It supports transcription by ear. More at: https://www.seventhstring.com/xscribe/overview.html

ScoreCloud

ScoreCloud is an online music notation service with transcription features such as:

Upload audio to generate sheet music
Transcribes polyphonic music with multiple instruments
Syncs sheet music to audio

Tools to edit and arrange transcribed scores

ScoreCloud produces high-quality sheet music output. The web interface makes it easy to transcribe, edit notation and share with others. More details at: https://scorecloud.com

Use Cases and Benefits

Music transcription enables musicians, students, teachers, composers, arrangers, and other music enthusiasts to convert audio recordings into standard music notation. This has a wide range of use cases and benefits:

Musicians can transcribe songs to learn them more easily. Seeing the notes written out makes it simpler to practice and memorize music. It also aids in analyzing chord progressions, melodies, harmonies, and overall song structure (https://speechify.com/blog/how-to-do-music-transcription/).

Students learning an instrument can transcribe pieces to improve their music reading and aural skills. Transforming audio to sheet music boosts understanding of rhythm, pitch, and musical phrasing (https://speechify.com/blog/mp3-to-sheet-music/).

Music teachers can create transcriptions to help students learn new repertoire. Having sheet music allows teaching songs more easily and aids in assessing student progress.

Composers and arrangers can transcribe their own compositions to notate them for sharing or performance. They may also make transcriptions of other works for analysis or to create new arrangements.

Overall, converting audio to standard notation opens up music to deeper study and wider accessibility. It enables preserving compositions for posterity and facilitates reproducing them accurately.

Limitations and Challenges

While music transcription technology has improved significantly in recent years, there are still some key limitations and challenges:

According to a research paper, one area where transcription struggles is with polyphonic music, complex rhythms, and ambiguous note durations. Transcribing the subtle nuances of human performances also remains difficult for AI systems.

Factors that affect accuracy include the quality of the audio, the genre and complexity of the music, and interference from background noise. Classical piano music tends to be easier to transcribe than heavily produced pop songs.

There are still improvements needed in transcribing harmony, handling repetition and variation in melodies, and recognizing higher-level musical structures. More robust training data and continued advances in deep learning will help address these challenges.

In summary, while great strides have been made, transcription is still not perfect. There is active research ongoing to handle polyphony, capture nuance, reduce errors, and better understand musical context and theory.

Tips for Effective Transcription

Capturing clean audio is crucial for improving transcription accuracy. As recommended by Ebby, “Try and record in a noise-free environment as much as possible. Speech should sound clear, without distortion or unexpected noise.”¹ Choosing the right instrumentation and musical style can also optimize results. Folk, pop, and classical pieces tend to transcribe better than complex jazz harmonies according to jonbjorkmusic.com. Acoustic rather than electric instruments also tend to be easier for algorithms to process and transcribe accurately.² Overall, aim for high quality recordings without background noise, distortion, or reverb when possible. Simpler musical arrangements with clear melodic lines and minimal overlapping parts will transcribe with greater accuracy.

The Future of Music Transcription

Music transcription technology is rapidly improving and is expected to become even more accurate and efficient in the coming years. According to research from Monash University, new innovations in animated music notation are playing a significant role in advancing transcription capabilities (The future of music notation in a digital world – Monash Lens).

Machine learning and AI are driving many of these improvements. Algorithms can now automatically transcribe music with a high degree of precision by analyzing audio waveforms. As these algorithms train on more data, they will become even better at identifying notes, instruments, rhythms, and other musical elements from recordings. This will enable faster and more accurate large-scale transcription of songs, albums, or entire music libraries (The Edge of the Future in Music Technology).

Looking ahead, transcription apps may even be able to detect multiple instrumental and vocal parts in polyphonic music. They could also identify the genre, mood, tempo, key signature, and other attributes of a recording. Integrating such metadata into the transcription would provide additional value to musicians. As natural language processing advances, apps may also generate written descriptions or analyses of the music to accompany the sheet music (Emerging Technologies and Trends in Audio Transcription).

In summary, rapid progress in music transcription technology means musicians can look forward to apps that are incredibly fast, accurate, and capable of conveying not just the notes, but the nuances and context of a musical performance.

Conclusion

Music transcription technology has come a long way in recent years. Apps like AnthemScore, Capo touch, and others can now effectively transcribe polyphonic music, complex instrumentation, and even vocals into written sheet music. The best apps utilize advanced audio analysis and machine learning to identify notes, rhythms, chords, and other musical elements from audio files or live recordings.

However, some challenges remain. Very complex polyphonic music with advanced harmonies can still confuse algorithms. Transcribing orchestral scores with many instruments remains difficult. And transcription accuracy relies heavily on audio recording quality. Despite great strides, technology has not yet matched the transcription capabilities of expert human transcribers.

Looking ahead, music transcription apps will likely continue leveraging AI advancements to handle more complexity. But human review and editing is still required for optimal accuracy. As long as apps focus on augmenting rather than replacing human transcribers, the technology outlook remains promising. Music transcription increasingly provides composers, musicians, and enthusiasts useful assistance in preserving and analyzing the music they love.

References

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