POLIPHONE: A Dataset for Smartphone Model Identification From Audio Recordings
When dealing with multimedia data, source attribution is a key challenge from a forensic perspective. This task aims to determine how a given content was captured, providing valuable insights for various applications, including legal proceedings and integrity investigations. The source attribution p...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10902157/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850233757722738688 |
|---|---|
| author | Davide Salvi Daniele Ugo Leonzio Antonio Giganti Claudio Eutizi Sara Mandelli Paolo Bestagini Stefano Tubaro |
| author_facet | Davide Salvi Daniele Ugo Leonzio Antonio Giganti Claudio Eutizi Sara Mandelli Paolo Bestagini Stefano Tubaro |
| author_sort | Davide Salvi |
| collection | DOAJ |
| description | When dealing with multimedia data, source attribution is a key challenge from a forensic perspective. This task aims to determine how a given content was captured, providing valuable insights for various applications, including legal proceedings and integrity investigations. The source attribution problem has been addressed in different domains, from identifying the camera model used to capture specific photographs to detecting the synthetic speech generator or microphone model used to create or record given audio tracks. Recent advancements in this area rely heavily on machine learning and data-driven techniques, which often outperform traditional signal processing-based methods. However, a drawback of these systems is their need for large volumes of training data, which must reflect the latest technological trends to produce accurate and reliable predictions. This presents a significant challenge, as the rapid pace of technological progress makes it difficult to maintain datasets that are up-to-date with real-world conditions. For instance, in the task of smartphone model identification from audio recordings, the available datasets are often outdated or acquired inconsistently, making it difficult to develop solutions that are valid beyond a research environment. In this paper we present POLIPHONE, a dataset for smartphone model identification from audio recordings. It includes data from 20 recent smartphones recorded in a controlled environment to ensure reproducibility and scalability for future research. The released tracks contain audio data from various domains (i.e., speech, music, environmental sounds), making the corpus versatile and applicable to a wide range of use cases. We also present numerous experiments to benchmark the proposed dataset using a state-of-the-art classifier for smartphone model identification from audio recordings. |
| format | Article |
| id | doaj-art-368fa67257084aea9367e3dcba646d5e |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-368fa67257084aea9367e3dcba646d5e2025-08-20T02:02:51ZengIEEEIEEE Access2169-35362025-01-0113370063701810.1109/ACCESS.2025.354515210902157POLIPHONE: A Dataset for Smartphone Model Identification From Audio RecordingsDavide Salvi0https://orcid.org/0000-0002-5163-3364Daniele Ugo Leonzio1https://orcid.org/0000-0002-3217-9952Antonio Giganti2https://orcid.org/0000-0003-4052-5138Claudio Eutizi3Sara Mandelli4https://orcid.org/0000-0003-3811-003XPaolo Bestagini5https://orcid.org/0000-0003-0406-0222Stefano Tubaro6https://orcid.org/0000-0002-1990-9869Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, ItalyDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, ItalyDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, ItalyDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, ItalyDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, ItalyDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, ItalyDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, ItalyWhen dealing with multimedia data, source attribution is a key challenge from a forensic perspective. This task aims to determine how a given content was captured, providing valuable insights for various applications, including legal proceedings and integrity investigations. The source attribution problem has been addressed in different domains, from identifying the camera model used to capture specific photographs to detecting the synthetic speech generator or microphone model used to create or record given audio tracks. Recent advancements in this area rely heavily on machine learning and data-driven techniques, which often outperform traditional signal processing-based methods. However, a drawback of these systems is their need for large volumes of training data, which must reflect the latest technological trends to produce accurate and reliable predictions. This presents a significant challenge, as the rapid pace of technological progress makes it difficult to maintain datasets that are up-to-date with real-world conditions. For instance, in the task of smartphone model identification from audio recordings, the available datasets are often outdated or acquired inconsistently, making it difficult to develop solutions that are valid beyond a research environment. In this paper we present POLIPHONE, a dataset for smartphone model identification from audio recordings. It includes data from 20 recent smartphones recorded in a controlled environment to ensure reproducibility and scalability for future research. The released tracks contain audio data from various domains (i.e., speech, music, environmental sounds), making the corpus versatile and applicable to a wide range of use cases. We also present numerous experiments to benchmark the proposed dataset using a state-of-the-art classifier for smartphone model identification from audio recordings.https://ieeexplore.ieee.org/document/10902157/Microphone identificationaudio recordingsource attributionaudio forensicsmulti-class classification |
| spellingShingle | Davide Salvi Daniele Ugo Leonzio Antonio Giganti Claudio Eutizi Sara Mandelli Paolo Bestagini Stefano Tubaro POLIPHONE: A Dataset for Smartphone Model Identification From Audio Recordings IEEE Access Microphone identification audio recording source attribution audio forensics multi-class classification |
| title | POLIPHONE: A Dataset for Smartphone Model Identification From Audio Recordings |
| title_full | POLIPHONE: A Dataset for Smartphone Model Identification From Audio Recordings |
| title_fullStr | POLIPHONE: A Dataset for Smartphone Model Identification From Audio Recordings |
| title_full_unstemmed | POLIPHONE: A Dataset for Smartphone Model Identification From Audio Recordings |
| title_short | POLIPHONE: A Dataset for Smartphone Model Identification From Audio Recordings |
| title_sort | poliphone a dataset for smartphone model identification from audio recordings |
| topic | Microphone identification audio recording source attribution audio forensics multi-class classification |
| url | https://ieeexplore.ieee.org/document/10902157/ |
| work_keys_str_mv | AT davidesalvi poliphoneadatasetforsmartphonemodelidentificationfromaudiorecordings AT danieleugoleonzio poliphoneadatasetforsmartphonemodelidentificationfromaudiorecordings AT antoniogiganti poliphoneadatasetforsmartphonemodelidentificationfromaudiorecordings AT claudioeutizi poliphoneadatasetforsmartphonemodelidentificationfromaudiorecordings AT saramandelli poliphoneadatasetforsmartphonemodelidentificationfromaudiorecordings AT paolobestagini poliphoneadatasetforsmartphonemodelidentificationfromaudiorecordings AT stefanotubaro poliphoneadatasetforsmartphonemodelidentificationfromaudiorecordings |