Acoustic Characterization of Rooms Using Reverberation Time Estimation Based on Supervised Learning Algorithm
The measurement of reverberation time is an essential procedure for the characterization of the acoustic performance of rooms. The values returned by these measurements allow us to predict how the sound will be transformed by the walls and furnishings of the rooms. The measurement of the reverberati...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/4/1661 |
| _version_ | 1797396784981475328 |
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| author | Giuseppe Ciaburro Gino Iannace |
| author_facet | Giuseppe Ciaburro Gino Iannace |
| author_sort | Giuseppe Ciaburro |
| collection | DOAJ |
| description | The measurement of reverberation time is an essential procedure for the characterization of the acoustic performance of rooms. The values returned by these measurements allow us to predict how the sound will be transformed by the walls and furnishings of the rooms. The measurement of the reverberation time is not an easy procedure to carry out and requires the use of a space in an exclusive way. In fact, it is necessary to use instruments that reproduce a sound source and instruments for recording the response of the space. In this work, an automatic procedure for estimating the reverberation time based on the use of artificial neural networks was developed. Previously selected sounds were played, and joint sound recordings were made. The recorded sounds were processed with the extraction of characteristics, then they were labeled by associating to each sound the value of the reverberation time in octave bands of that specific room. The obtained dataset was used as input for the training of an algorithm based on artificial neural networks. The results returned by the predictive model suggest using this methodology to estimate the reverberation time of any closed space, using simple audio recordings without having to perform standard measurements or calculate the integration explicitly. |
| first_indexed | 2024-03-09T00:56:32Z |
| format | Article |
| id | doaj.art-6291cb61fcd64583b20dbbec87a23e4f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T00:56:32Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6291cb61fcd64583b20dbbec87a23e4f2023-12-11T16:51:06ZengMDPI AGApplied Sciences2076-34172021-02-01114166110.3390/app11041661Acoustic Characterization of Rooms Using Reverberation Time Estimation Based on Supervised Learning AlgorithmGiuseppe Ciaburro0Gino Iannace1Department of Architecture and Industrial Design, Università degli Studi della Campania “Luigi Vanvitelli”, Borgo San Lorenzo, 81031 Aversa (Ce), ItalyDepartment of Architecture and Industrial Design, Università degli Studi della Campania “Luigi Vanvitelli”, Borgo San Lorenzo, 81031 Aversa (Ce), ItalyThe measurement of reverberation time is an essential procedure for the characterization of the acoustic performance of rooms. The values returned by these measurements allow us to predict how the sound will be transformed by the walls and furnishings of the rooms. The measurement of the reverberation time is not an easy procedure to carry out and requires the use of a space in an exclusive way. In fact, it is necessary to use instruments that reproduce a sound source and instruments for recording the response of the space. In this work, an automatic procedure for estimating the reverberation time based on the use of artificial neural networks was developed. Previously selected sounds were played, and joint sound recordings were made. The recorded sounds were processed with the extraction of characteristics, then they were labeled by associating to each sound the value of the reverberation time in octave bands of that specific room. The obtained dataset was used as input for the training of an algorithm based on artificial neural networks. The results returned by the predictive model suggest using this methodology to estimate the reverberation time of any closed space, using simple audio recordings without having to perform standard measurements or calculate the integration explicitly.https://www.mdpi.com/2076-3417/11/4/1661reverberation timesound recordingsound fieldsspatial coherenceartificial neural networkLevenberg–Marquardt algorithm |
| spellingShingle | Giuseppe Ciaburro Gino Iannace Acoustic Characterization of Rooms Using Reverberation Time Estimation Based on Supervised Learning Algorithm Applied Sciences reverberation time sound recording sound fields spatial coherence artificial neural network Levenberg–Marquardt algorithm |
| title | Acoustic Characterization of Rooms Using Reverberation Time Estimation Based on Supervised Learning Algorithm |
| title_full | Acoustic Characterization of Rooms Using Reverberation Time Estimation Based on Supervised Learning Algorithm |
| title_fullStr | Acoustic Characterization of Rooms Using Reverberation Time Estimation Based on Supervised Learning Algorithm |
| title_full_unstemmed | Acoustic Characterization of Rooms Using Reverberation Time Estimation Based on Supervised Learning Algorithm |
| title_short | Acoustic Characterization of Rooms Using Reverberation Time Estimation Based on Supervised Learning Algorithm |
| title_sort | acoustic characterization of rooms using reverberation time estimation based on supervised learning algorithm |
| topic | reverberation time sound recording sound fields spatial coherence artificial neural network Levenberg–Marquardt algorithm |
| url | https://www.mdpi.com/2076-3417/11/4/1661 |
| work_keys_str_mv | AT giuseppeciaburro acousticcharacterizationofroomsusingreverberationtimeestimationbasedonsupervisedlearningalgorithm AT ginoiannace acousticcharacterizationofroomsusingreverberationtimeestimationbasedonsupervisedlearningalgorithm |