A Real-Time System for Measuring Sound Goodness in Instrumental Sounds

Romani Picas O, Parra Rodriguez H, Dabiri D, Tokuda H, Hariya W, Oishi K, Serra X. A Real-Time System for Measuring Sound Goodness in Instrumental Sounds. In: AES Convention 138. Audio Engineering Society; 2015. Paper 9350. Available from: https://aes.org/publications/elibrary-page/?id=17774

@inproceedings{RomaniPicas2015_17774,
  author = {Romani Picas, Oriol and Parra Rodriguez, Hector and Dabiri, Dara and Tokuda, Hiroshi and Hariya, Wataru and Oishi, Koji and Serra, Xavier},
  title = {{A Real-Time System for Measuring Sound Goodness in Instrumental Sounds}},
  booktitle = {AES Convention 138},
  note = {Paper 9350},
  year = {2015},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=17774}
}

TY  - CPAPER
TI  - A Real-Time System for Measuring Sound Goodness in Instrumental Sounds
AU  - Romani Picas, Oriol
AU  - Parra Rodriguez, Hector
AU  - Dabiri, Dara
AU  - Tokuda, Hiroshi
AU  - Hariya, Wataru
AU  - Oishi, Koji
AU  - Serra, Xavier
T2  - AES Convention 138
M1  - Paper 9350
PY  - 2015
DA  - 2015/05/06
UR  - https://aes.org/publications/elibrary-page/?id=17774
PB  - Audio Engineering Society
LA  - en
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.
ER  -