date-added: 2017-12-22 18:53:42 +0000 date-modified: 2017-12-22 19:03:05 +0000 keywords: piano gesture recognition, optical sensor, real-time data acquisition, bela, music informatics local-url: https://pdfs.semanticscholar.org/fd00/fcfba2f41a3f182d2000ca4c05fb2b01c475.pdf publisher-url: http://homes.create.aau.dk/dano/nime17/ bdsk-url-1: http://www.nime.org/proceedings/2017/nime2017_paper0062.pdfdate-added: 2017-12-22 18:53:42 +0000 date-modified: 2017-12-22 19:03:05 +0000 keywords: piano gesture recognition, optical sensor, real-time data acquisition, bela, music informatics local-url: https://pdfs.semanticscholar.org/fd00/fcfba2f41a3f182d2000ca4c05fb2b01c475.pdf publisher-url: http://homes.create.aau.dk/dano/nime17/ bdsk-url-1: http://www.nime.org/proceedings/2017/nime2017_paper0062.pdfdate-added: 2017-12-22 18:53:42 +0000 date-modified: 2017-12-22 19:03:05 +0000 keywords: piano gesture recognition, optical sensor, real-time data acquisition, bela, music informatics local-url: https://pdfs.semanticscholar.org/fd00/fcfba2f41a3f182d2000ca4c05fb2b01c475.pdf publisher-url: http://homes.create.aau.dk/dano/nime17/ bdsk-url-1: http://www.nime.org/proceedings/2017/nime2017_paper0062.pdfdate-added: 2017-12-22 18:53:42 +0000 date-modified: 2017-12-22 19:03:05 +0000 keywords: piano gesture recognition, optical sensor, real-time data acquisition, bela, music informatics local-url: https://pdfs.semanticscholar.org/fd00/fcfba2f41a3f182d2000ca4c05fb2b01c475.pdf publisher-url: http://homes.create.aau.dk/dano/nime17/ bdsk-url-1: http://www.nime.org/proceedings/2017/nime2017_paper0062.pdfdate-added: 2017-12-22 18:53:42 +0000 date-modified: 2017-12-22 19:03:05 +0000 keywords: piano gesture recognition, optical sensor, real-time data acquisition, bela, music informatics local-url: https://pdfs.semanticscholar.org/fd00/fcfba2f41a3f182d2000ca4c05fb2b01c475.pdf publisher-url: http://homes.create.aau.dk/dano/nime17/ bdsk-url-1: http://www.nime.org/proceedings/2017/nime2017_paper0062.pdfThis paper presents the results of a study of piano pedalling techniques on the sustain pedal using a newly designed measurement system named Piano Pedaller. The system is comprised of an optical sensor mounted in the piano pedal bearing block and an embedded platform for recording audio and sensor data. This enables recording the pedalling gesture of real players and the piano sound under normal playing conditions. Using the gesture data collected from the system, the task of classifying these data by pedalling technique was undertaken using a Support Vector Machine (SVM). Results can be visualised in an audio based score following application to show pedalling together with the player’s position in the score
