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O. Romani Picas, H. Parra Rodriguez, D. Dabiri, H. Tokuda, W. Hariya, K. Oishi, and X. Serra, "A Real-Time System for Measuring Sound Goodness in Instrumental Sounds," Paper 9350, (2015 May.). doi: O. Romani Picas, H. Parra Rodriguez, D. Dabiri, H. Tokuda, W. Hariya, K. Oishi, and X. Serra, "A Real-Time System for Measuring Sound Goodness in Instrumental Sounds," Paper 9350, (2015 May.). doi:
Abstract: This paper presents a system that complements the tuner functionality by evaluating the sound quality of a music performer in real-time. It consists of a software tool that computes a score of how well single notes are played with respect to a collection of reference sounds. To develop such a tool we first record a collection of single notes played by professional performers. Then, the collection is annotated by music teachers in terms of the performance quality of each individual sample. From the recorded samples, several audio features are extracted and a machine learning method is used to find the features that best described performance quality according to musician's annotations. An evaluation is carried out to assess the correlation between systems’ predictions and musicians’ criteria. Results show that the system can reasonably predict musicians’ annotations of performance quality.

@article{romani picas2015a,
author={romani picas, oriol and parra rodriguez, hector and dabiri, dara and tokuda, hiroshi and hariya, wataru and oishi, koji and serra, xavier},
journal={journal of the audio engineering society},
title={a real-time system for measuring sound goodness in instrumental sounds},
year={2015},
volume={},
number={},
pages={},
doi={},
month={may},} @article{romani picas2015a,
author={romani picas, oriol and parra rodriguez, hector and dabiri, dara and tokuda, hiroshi and hariya, wataru and oishi, koji and serra, xavier},
journal={journal of the audio engineering society},
title={a real-time system for measuring sound goodness in instrumental sounds},
year={2015},
volume={},
number={},
pages={},
doi={},
month={may},
abstract={this paper presents a system that complements the tuner functionality by evaluating the sound quality of a music performer in real-time. it consists of a software tool that computes a score of how well single notes are played with respect to a collection of reference sounds. to develop such a tool we first record a collection of single notes played by professional performers. then, the collection is annotated by music teachers in terms of the performance quality of each individual sample. from the recorded samples, several audio features are extracted and a machine learning method is used to find the features that best described performance quality according to musician's annotations. an evaluation is carried out to assess the correlation between systems’ predictions and musicians’ criteria. results show that the system can reasonably predict musicians’ annotations of performance quality.},}

TY - paper
TI - A Real-Time System for Measuring Sound Goodness in Instrumental Sounds
SP - EP -
AU - Romani Picas, Oriol
AU - Parra Rodriguez, Hector
AU - Dabiri, Dara
AU - Tokuda, Hiroshi
AU - Hariya, Wataru
AU - Oishi, Koji
AU - Serra, Xavier
PY - 2015
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2015 TY - paper
TI - A Real-Time System for Measuring Sound Goodness in Instrumental Sounds
SP - EP -
AU - Romani Picas, Oriol
AU - Parra Rodriguez, Hector
AU - Dabiri, Dara
AU - Tokuda, Hiroshi
AU - Hariya, Wataru
AU - Oishi, Koji
AU - Serra, Xavier
PY - 2015
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2015
AB - This paper presents a system that complements the tuner functionality by evaluating the sound quality of a music performer in real-time. It consists of a software tool that computes a score of how well single notes are played with respect to a collection of reference sounds. To develop such a tool we first record a collection of single notes played by professional performers. Then, the collection is annotated by music teachers in terms of the performance quality of each individual sample. From the recorded samples, several audio features are extracted and a machine learning method is used to find the features that best described performance quality according to musician's annotations. An evaluation is carried out to assess the correlation between systems’ predictions and musicians’ criteria. Results show that the system can reasonably predict musicians’ annotations of performance quality.