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AUTOMATIC TRANSCRIPTION OF MUSIC

Anssi P. Klapuri

Open publisher page 90 citations

Abstract

A system for the automatic transcription of music is described. Signal processing methods are introduced that solve different facets of the overall problem. Main emphasis is laid on finding the multiple pitches of concurrent musical sounds. Sound onset detection and musical meter estimation are described to some extent. Other topics discussed are noise robustness, estimation of the number of concurrent voices, sound separation, and musical instrument recognition. The presented system is evaluated using a database of musical sounds, synthesized MIDI-songs, and CDrecordings. Also, the performance of the system is compared to that of human listeners. 1.

About this research paper

What this paper is about

A system for the automatic transcription of music is described. Signal processing methods are introduced that solve different facets of the overall problem. Main emphasis is laid on finding the multiple pitches of concurrent musical sounds. Sound onset detection and musical meter estimation are described to some extent. Other topics discussed are noise robustness, estimation of the number of concurrent voices, sound separation, and musical instrument recognition. The presented system is evaluated using a database of musical sounds, synthesized MIDI-songs, and CDrecordings. Also, the performance of the system is compared to that of human listeners. 1.

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OpenAlex reports 90 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

A system for the automatic transcription of music is described. Signal processing methods are introduced that solve different facets of the overall problem. Main emphasis is laid on finding the multiple pitches of concurrent musical sounds. Sound onset detection and musical meter estimation are described to some extent. Other topics discussed are noise robustness, estimation of the number of concurrent voices, sound separation, and musical instrument recognition. The presented system is evaluated using a database of musical sounds, synthesized MIDI-songs, and CDrecordings. Also, the performance of the system is compared to that of human listeners. 1.

Key concepts: MIDI, Transcription (linguistics), Computer science, Speech recognition, Representation (politics), Musical, Task (project management), Music information retrieval

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